Mercari's Price Suggestion and Modelling¶
Mercari is an online shopping marketplace which is powered by one of the biggest community of Japan where users can sell pretty much anything.The community wants to offer price suggestions to the sellers but is a tough task as the sellers are enabled to put just about anything, or any bundle of things, on Mercari’s marketplace.
Problem Statement:¶
- It can be hard to know how much something’s really worth. Small details can mean big differences in pricing. For example, one of these sweaters cost 335 dollars and the other cost 9.99 dollars. Can you guess which one’s which?
Sweater A: Vince, Long sleeve, Turtle neck pullover sweater, Black, Size L, Great condition
Sweater B: St. John's Bay Long sleeve, Turtle neck Pullover sweater, Size L, Great condition
Hence, it's harder to guess or decide what price which product should have
Product pricing gets even harder at scale, considering just how many products are sold online. Clothing has strong seasonal pricing trends and is heavily influenced by brand names, while electronics have fluctuating prices based on product specs.
Challenges to Solve:¶
Given details about a product like product category name, brand name, and item condition, our challenge is to build an algorithm that automatically suggests the right product prices
But if solved rightly, it can eliminate human interference in giving price suggestions of a product and speed up efficiency of the shopping app. That’s when Machine Learning comes to play.
The task of this Project is to build an algorithm that suggests the right product prices for shopping app from product name, user inputted text descriptions of the product, category name, brand name, item condition, and shipping information.
The goal is about creating a model that would help sellers to price their products.Pricing should be intermediate between sellers and buyers.
Our Objectives:¶
- • Data Cleaning • Pre-processing • Feature Engineering
- Exploratory Data Analysis:
- Gathering as many insights as possible through Exploratory Data Analysis.
- Trying to see through Descriptive Statistics that what our data is trying to tell us and understand the significance of each and every variable.
- Text Processing:
- Natural Language Processing (NLP)
- Tokenizing and tf-idf algorithm
- K-means Clustering
- The given problem is exactly about predicting the price, which is a real-valued value, thus it falls into the Regression Category
- Trying to provide some interpretability and evolving our ways to handle the upcoming problems as we proceed through our project
Models used in the Project:¶
- Ordinary Least Squares Regression
- Linear Regression
- Ridge Regression
- Stochastic Gradient Descent Regressor
- Random Forest Regressor
Data Description:¶
train_id- the id of the productname- the name of the productitem_condition_id- the condition of the product provided by the sellerAccording to information available on Mercari Website
- 1 stands for new
- 2 stands for fairly new
- 3 stands for Good
- 4 stands for Bad
- 5 stands for Very Poor
category_name- category of the productbrand_name- brand name of the productprice- the price that the product was sold for. (This is the target variable that we will predict) The unit is USDshipping- 1 if shipping fee is paid by seller and 0 by buyeritem_description- the full description of the item
Independent variables: train_id, name, item_condition_id, category_name, brand_name, shipping, item_description
Target Variable: price
We will build various supervised machine learning regression models and see which succeeds in solving the given mapping between the input variables and the price feature in the best way. Let’s begin in a step-by-step manner
Step 1: Exploratory Data Analysis¶
- The very first step in solving any case study in data science is to properly look and analyze the data you have.
- It helps to give valuable insights into the pattern and information it has to convey.
- Statistical tools have a big role in proper visualization of the data.
- Even though it is considered not a very important part of solving a problem, but successful data scientists and ML engineers spend maximum part of solving a problem by analyzing the data they have.
- Proper EDA gives interesting features of your data which in turn influences our data preprocessing and model selection criterion as well.
Importing the libraries:¶
In [3]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from collections import Counter
import nltk
from nltk.corpus import stopwords
from wordcloud import WordCloud
import re
import warnings
warnings.filterwarnings('ignore') # to ignore the warnings
Loading the data:¶
In [4]:
data = pd.read_csv('../input/mercari-dataset/train2.csv', encoding='latin1')
# As most of the words are from Western European Language hence encoding='latin1'
# Latin-1, also called ISO-8859-1, is an 8-bit character set endorsed by the
# International Organization for Standardization (ISO) and represents the alphabets
# of Western European languages. As its name implies, it is a subset of ISO-8859,
# which includes several other related sets for writing systems like Cyrillic, Hebrew, and Arabic.
# It is used by most Unix systems as well as Windows. DOS and Mac OS, however, use their own sets.
In [5]:
data.head()
Out[5]:
| train_id | name | item_condition_id | category_name | brand_name | price | shipping | item_description | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0 | Hero 77 fountain pen | 2 | Other/Office supplies/Writing | NaN | 12.0 | 1 | For sale a brand new Hero 77 fountain pen, doe... |
| 1 | 1 | 14K Yellow Gold Earrings | 3 | Women/Jewelry/Earrings | NaN | 20.0 | 0 | 14k black Onyx earrings Good condition Final sale |
| 2 | 2 | New balance 2-in 1 size S dry fit shorts | 2 | Women/Athletic Apparel/Shorts | New Balance | 10.0 | 0 | Brand new never worn, but I tore the tag off w... |
| 3 | 3 | Zella black workout tank w mesh cut out | 3 | Women/Athletic Apparel/Shirts & Tops | Zella | 15.0 | 1 | Zella black workout tank with mesh cut outs. |
| 4 | 4 | NWT Lilly Pulitzer gabby dress sz 8 | 1 | Women/Dresses/Above Knee, Mini | Lilly Pulitzer | 75.0 | 0 | New with tags!! Size 8. |
In [6]:
# Data Dimensions
data.shape
Out[6]:
(148253, 8)
As the orignal data had over 1.4 million observations, we only take the 10% out of it in the csv file above
In [7]:
# Data Types
data.dtypes
Out[7]:
train_id int64 name object item_condition_id int64 category_name object brand_name object price float64 shipping int64 item_description object dtype: object
In [8]:
# Columns in the data
data.columns
Out[8]:
Index(['train_id', 'name', 'item_condition_id', 'category_name', 'brand_name',
'price', 'shipping', 'item_description'],
dtype='object')
In [9]:
# Checking the NAs in the data
data.isnull().sum()
Out[9]:
train_id 0 name 0 item_condition_id 0 category_name 641 brand_name 62991 price 0 shipping 0 item_description 0 dtype: int64
In [10]:
# making a copy of the data as backup
data1 = data.copy()
Handling missing values:¶
In [11]:
for value in ['category_name']:
data1[value].fillna(value='Other', inplace=True)
# replacing by "Other" because there already exists a category by the same name
for value in ['brand_name']:
data1[value].fillna(value='Unknown', inplace=True)
# Rechecking the null values
data1.isnull().sum()
Out[11]:
train_id 0 name 0 item_condition_id 0 category_name 0 brand_name 0 price 0 shipping 0 item_description 0 dtype: int64
In [33]:
data1.price.describe()
Out[33]:
count 148253.000000 mean 26.807933 std 39.358186 min 0.000000 25% 10.000000 50% 17.000000 75% 29.000000 max 2000.000000 Name: price, dtype: float64
In [34]:
plt.figure(figsize=(17,5))
plt.title('Price Distribution', fontsize=15)
sns.boxplot(data1.price, showfliers=False)
plt.xlabel('Price',fontsize=15)
plt.show()
Insight 1 from Price
- mean = 26, median = 17, min = 0, max = 2000
- 25% of the products are priced below 10 dollars
- 50% of products are priced below 17 dollars
- 75% of products are priced below 29 dollars
- Also, the maximum price that any product has is 2000 dollars
In [35]:
plt.plot(figsize=(30,20))
sns.distplot(data1['price'])
Out[35]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f8675e09cd0>
- It can be concluded that the distribution of the
pricevariable is heavily right-skewed. - The
pricevariable follows a skewed distribution and in order to make errors on low price product more relevant than for higher prices, we take the log transform - log1p = log(p) + 1, we add 1 to avoid zero(log0=infinity) and negative values
Our dependent variable should be normally distributed i.e. The Assumption of Normality should be satisfied
Let's compare both the graphs side by side
In [36]:
plt.subplot(1, 2, 1)
(data1['price']).plot.hist(bins=50, figsize=(12, 6), edgecolor = 'white', range = [0, 250])
plt.xlabel('price', fontsize=12)
plt.title('Price Distribution', fontsize=12)
plt.subplot(1, 2, 2)
np.log1p(data1['price']).plot.hist(bins=50, figsize=(12, 6), edgecolor='white')
plt.xlabel('log(price)+1', fontsize=12)
plt.title('Price Distribution', fontsize=12)
Out[36]:
Text(0.5, 1.0, 'Price Distribution')
2. Shipping:¶
In [37]:
data1['shipping'].value_counts(normalize=True) # to show as a percentage of total values
Out[37]:
0 0.553587 1 0.446413 Name: shipping, dtype: float64
In [38]:
index = ['Buyer','Seller']
values = data1['shipping'].value_counts()
plt.figure(figsize=(7,4))
plt.pie(values,startangle=90,autopct='%0.1f%%',explode=(0,0.1))
plt.legend(title = "Shipping Paid by",loc = "upper right",labels= index,fontsize=10)
plt.tight_layout()
plt.title("Analysis on Shipping Paid",fontsize=20)
Out[38]:
Text(0.5, 1.0, 'Analysis on Shipping Paid')
Insight 1 from shipping:
0- 82071 times (buyer charged)55.53%1- 66182 times(seller charged)44.64%- Over 55% of items' shipping fee were paid by the buyers
Suggestion to the seller:
- The above insight matches with our perception that the sellers need to keep a lower price to compensate for the additional shipping
To check how shipping is related to the price:¶
In [39]:
buyer_charged = data1.loc[data1['shipping'] == 0, 'price']
seller_charged = data1.loc[data1['shipping'] == 1, 'price']
fig, ax = plt.subplots(figsize=(14, 8))
ax.hist(buyer_charged, bins=30, range=[0, 100], label='Price when buyer paid shipping', alpha=0.5, color='b')
ax.hist(seller_charged, bins=30, range=[0, 100], label='Price when seller paid shipping', alpha=0.5, color='r')
plt.title('Price Distribution by shipping type', fontsize = 20)
plt.xlabel('Price', fontsize = 15)
plt.ylabel('No. of Items', fontsize = 15)
plt.legend(fontsize = 15)
plt.show()
Insight 2 from shipping:
- The price when buyer pays is high compared to when seller pays
Chekcing the Actual values -
In [40]:
# average price for shipping for seller and buyer
print('The average price is {}'.format(round(seller_charged.mean(), 2)), 'if seller pays shipping')
print('The average price is {}'.format(round(buyer_charged.mean(), 2)), 'if buyer pays shipping')
The average price is 22.82 if seller pays shipping The average price is 30.02 if buyer pays shipping
3. Category Name:¶
Unique Category Names
In [41]:
print('There are', data1['category_name'].nunique(), 'unique values in category name column')
There are 1052 unique values in category name column
Top 10 most common category names
In [42]:
data1['category_name'].value_counts()[:10]
Out[42]:
Women/Athletic Apparel/Pants, Tights, Leggings 6049 Women/Tops & Blouses/T-Shirts 4686 Beauty/Makeup/Face 3429 Beauty/Makeup/Lips 3025 Electronics/Video Games & Consoles/Games 2587 Beauty/Makeup/Eyes 2560 Electronics/Cell Phones & Accessories/Cases, Covers & Skins 2464 Women/Underwear/Bras 2088 Women/Tops & Blouses/Blouse 2073 Women/Dresses/Above Knee, Mini 2029 Name: category_name, dtype: int64
General Observations from the above insights:
- It is seen in this table that a subcategory in the category name is seperated by a slash "/"
For ex. Beauty/Makeup/Face and Beauty/Makeup/Lips
This means
Beautycategory has a subcategoryMakeUpand this sub categoryMakeUpis further divided intoFaceandLipsIt is also observed that
Womenapparel has the maximum number of items followed by any other category.- The category names are listed by ‘/’ delimiter which tells about the main category, sub-category 1 and sub-category 2 of the products.
- Therefore, to get better idea of each product, we will do feature engineering here and split the category name into 3 different columns namely, ‘Main_categ’, ‘sub_categ1’ and ‘sub_categ2’.
Divide category names into Main category, Sub-category 1 and Sub-category 2:
In [12]:
temp = data1[data1['brand_name']!='Unknown'] # remove Unknown coz it represents missing values
data1[['Main_categ','sub_categ1','sub_categ2']] = data1.category_name.str.split("/",expand = True,n= 2)
for i in ['sub_categ1','sub_categ2']:
data1[i].fillna(value = "Label not given", inplace=True)
After splitting the category_name column, the unique items that I have in each of the newly formed columns is listed below:
In [13]:
print('There are', data1['Main_categ'].nunique(), 'unique values in Main category')
print('There are', data1['sub_categ1'].nunique(), 'unique values in Sub-category 1')
print('There are', data1['sub_categ2'].nunique(), 'unique values in Sub-category 2')
There are 10 unique values in Main category There are 114 unique values in Sub-category 1 There are 741 unique values in Sub-category 2
3.1 Main Category:¶
Let’s find out which of the products rank the highest in terms of frequency of occurrence:
In [45]:
maincat_count = data1.Main_categ.value_counts()
plt.figure(figsize=(15, 10))
sns.barplot(maincat_count.index[0:11], maincat_count[0:11])
plt.title('Main category Analysis',fontsize = 20)
plt.xlabel('Main category',fontsize = 15)
plt.ylabel('Count',fontsize = 15)
plt.xticks(rotation=15, fontsize=12)
plt.show()
Insight 1 from Main Category:
- It can be said that
Womenproducts occur with the maximum frequency, followed byBeautyproducts. The 3rd largest general category is owned byKidsproducts
Main category vs Price:¶
In [46]:
temp = data1[data1['price'] < 80]
plt.figure(figsize=(15, 10))
sns.boxplot(temp['Main_categ'], temp['price'])
plt.title('BoxPlot of Main category vs Price', fontsize = 20)
plt.xlabel('Main Category',fontsize = 15)
plt.ylabel('Price',fontsize = 15)
plt.xticks(rotation = 15, fontsize=12)
plt.show()
In [47]:
temp.groupby(['Main_categ'])['price'].agg('median')
Out[47]:
Main_categ Beauty 15.0 Electronics 14.0 Handmade 11.0 Home 18.0 Kids 14.0 Men 20.0 Other 13.0 Sports & Outdoors 16.0 Vintage & Collectibles 15.0 Women 19.0 Name: price, dtype: float64
Insight 2 from Main category:
- Although there are maximum products purchased in the
Women'scategory, the price ofMencategory products(20 dollars) is almost as expensive as the products inWomencategory(19 dollars)
Sub-category 1:¶
As there are 114 unique sub categories, we won't be able to plot them all, hence, first we need to seperate the top 10 Sub-categories
In [48]:
index = []
[index.append(key) for key, value in Counter(data1['sub_categ1']).most_common()]
top_10 = index[:10]
temp = data1[data1['sub_categ1'].isin(top_10)]
# the corresponding top 10 indexes get their corresponding names from "sub_categ1"
In [49]:
plt.figure(figsize=(20,10))
sns.countplot(temp['sub_categ1'])
plt.title('Sub-Category 1 Analysis',fontsize = 20)
plt.xticks(rotation = 12,wrap = True,fontsize = 15)
plt.xlabel('Top 10 Sub-Category1',fontsize = 15)
plt.ylabel('Frequency',fontsize = 15)
Out[49]:
Text(0, 0.5, 'Frequency')
Insight 1 from Sub-category 1:
- Most of the products from Sub-category 1 are bought from
Athletic ApparelandMakeupcategory
Sub-category 1 vs Price:¶
In [50]:
temp2 = temp[temp['price']<80] # box plot
plt.figure(figsize=(20,10))
sns.boxplot(temp2['sub_categ1'],temp2['price'])
plt.title('Sub-Category 1 vs Price',fontsize = 20)
plt.xticks(rotation = 10,wrap = True,fontsize = 15)
plt.xlabel('Top 10 Sub-Category 1',fontsize = 15)
plt.ylabel('Price',fontsize = 15)
Out[50]:
Text(0, 0.5, 'Price')
In [51]:
temp2.groupby(['sub_categ1'])['price'].agg('median')
Out[51]:
sub_categ1 Athletic Apparel 21.0 Cell Phones & Accessories 10.0 Dresses 20.0 Jewelry 13.0 Makeup 15.0 Shoes 26.0 Tops & Blouses 14.0 Toys 14.0 Women's Accessories 16.0 Women's Handbags 25.0 Name: price, dtype: float64
Insight 2 from Sub-category 1:
- Products in
Shoescategory is more expensive thanWomen's Handbagby 1 dollar - Products in
Women's Handbagcategory is more expensive thus has low frequency - Hence its satisfies the logic that Expensive products are bought less number of times
3.3 Sub-category 2:¶
In [52]:
index = []
[index.append(key) for key, value in Counter(data1['sub_categ2']).most_common()]
top_10 = index[:10]
temp = data1[data1['sub_categ2'].isin(top_10)]
plt.figure(figsize=(20,10))
sns.countplot(temp['sub_categ2'])
plt.title('Sub-Category 2 Analysis',fontsize = 20)
plt.xticks(rotation = 7,wrap = True,fontsize = 14)
plt.xlabel('Top 10 Sub-Category 2',fontsize = 15)
plt.ylabel('Frequency',fontsize = 15)
Out[52]:
Text(0, 0.5, 'Frequency')
Insight 1 from Sub-category 2:
- Products under category
Pants,Tights,Leggingsare bought the highest number of times
Sub-category 2 vs Price:¶
In [53]:
temp2 = temp[temp['price']<80]
plt.figure(figsize=(20,10))
sns.boxplot(temp2['sub_categ2'],temp2['price'])
plt.title('Sub-Category 2 vs Price', fontsize = 20)
plt.xticks(rotation = 10,wrap = True,fontsize = 14)
plt.xlabel('Top 10 Sub-Category 2', fontsize = 15)
plt.ylabel('Price', fontsize = 15)
Out[53]:
Text(0, 0.5, 'Price')
In [54]:
temp2.groupby(['sub_categ2'])['price'].agg('median')
Out[54]:
sub_categ2 Athletic 36.0 Cases, Covers & Skins 10.0 Eyes 12.0 Face 15.0 Games 15.0 Lips 14.0 Other 15.0 Pants, Tights, Leggings 28.0 Shoes 18.0 T-Shirts 15.0 Name: price, dtype: float64
Insight 2 from Sub-Category 2:
- Yet again products in the
Athleticcategory are bought moderately as they are expensive - Products in
Pants,Tights,Leggingscategory are bought more as moderately expensive
4. Brand Name:¶
In [55]:
print('There are', data1['brand_name'].nunique(), 'unique values in brand name column')
There are 2321 unique values in brand name column
As there are 2321 unique brand names, we lookout only for products purchased from the top 10 frequently occuring brands
In [56]:
index = []
[index.append(key) for key, value in Counter(data1['brand_name']).most_common()]
top_10 = index[:10]
temp = data1[data1['brand_name'].isin(top_10)]
plt.figure(figsize=(20,20))
plt.subplot(2,1,1)
sns.countplot(temp['brand_name'])
plt.title('Brand wise Analysis (with Unknown)', fontsize = 25)
plt.xticks(rotation = 0,wrap = True,fontsize = 14)
plt.xlabel('Brand Name',fontsize = 0)
plt.ylabel('Frequency', fontsize = 20)
temp = data1[data1['brand_name']!='Unknown']
index = []
[index.append(key) for key, value in Counter(temp['brand_name']).most_common()]
top_10 = index[:10]
temp2 = temp[temp['brand_name'].isin(top_10)]
plt.subplot(2,1,2)
# plt.figure(figsize=(40,20))
sns.countplot(temp2['brand_name'])
plt.title('Brand wise Analysis (without Unknown)', fontsize = 25)
plt.xticks(rotation = 0,wrap = True,fontsize = 14)
plt.xlabel('Brand Name',fontsize = 20)
plt.ylabel('Frequency',fontsize = 20)
plt.show()
Insight 1 from Brand name:
- We ignore the Brand name marked as
Unknownas it represents our missing values - Hence we make the graph again without considering the
Unknown - We observe that
Pink,Nike, andVictoria's Secretare top 3 most purchased brands - Also it is obvious that
PinkandVictoria's Secretbrand products are mostly bought fromWomenmain category more frequently
Brand name vs Price:¶
In [57]:
temp3 = temp2[temp2['price']<80]
plt.figure(figsize=(20,10))
sns.boxplot(temp3['brand_name'],temp3['price'])
plt.title("Top 10 brand wise Analysis (According to price)",fontsize=20)
plt.xlabel("Brand Name",fontsize=15)
plt.ylabel("Price",fontsize=15)
plt.xticks(rotation = 0,wrap = True,fontsize = 15)
Out[57]:
(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), <a list of 10 Text major ticklabel objects>)
In [58]:
temp3.groupby(['brand_name'])['price'].agg('median')
Out[58]:
brand_name American Eagle 14.0 Apple 15.0 FOREVER 21 12.0 LuLaRoe 29.0 Lululemon 35.0 Michael Kors 36.0 Nike 21.0 Nintendo 19.0 PINK 20.0 Victoria's Secret 19.0 Name: price, dtype: float64
Insight 2 from Brand name:
Michael Korsproducts cost high hence less boughtLululemonhas the 2nd highest median pricePink,NikeandVictoria's Secretproducts have moderate pricing hence are more frequently bought
5. Item Description:¶
First we create a function to return us a count of words from each Description
In [59]:
def length(description):
count = 0
for i in description.split():
count+=1
return count
Then we create a series which has the description and the length of description in words
In [60]:
lol=[]
for i in data1['item_description']:
temp=[]
temp.append(i)
temp.append(length(str(i)))
lol.append(temp)
Printing first three rows to check the string and the number of words in the string match or not
In [61]:
print(lol[0:3])
[['For sale a brand new Hero 77 fountain pen, does not include the ink.', 14], ['14k black Onyx earrings Good condition Final sale', 8], ['Brand new never worn, but I tore the tag off when I tried them on. Double layer spandex shorts to keep you dry and covered!', 25]]
Making a new dataframe of the series we created - lol
In [62]:
mydf = pd.DataFrame(lol, columns=['Description', 'Description_length'])
print(mydf.head(2))
Description Description_length 0 For sale a brand new Hero 77 fountain pen, doe... 14 1 14k black Onyx earrings Good condition Final sale 8
Adding only 2nd coloumn Description_length from mydf to data1
In [63]:
data1['Description_length'] = mydf['Description_length']
In [64]:
plt.figure(figsize=(10,5))
sns.scatterplot(x=data1.Description_length, y=data1.Description_length.value_counts())
plt.title('Scatter-plot of description length',fontsize=20)
plt.xlabel("Description Length in words",fontsize=15)
plt.ylabel("Frequency",fontsize=15)
Out[64]:
Text(0, 0.5, 'Frequency')
Insight 1 from Item Description:
- Most of the item descriptions are written in the range of 3-50 words
Item Description vs Price:¶
In [65]:
temp4=data1[data1['Description_length']<500]
plt.figure(figsize=(15,5))
sns.scatterplot(x=temp4['Description_length'], y=data1.price)
plt.title('Description length vs Price',fontsize=20)
plt.xlabel("Description Length in words",fontsize=15)
plt.ylabel("Price",fontsize=15)
Out[65]:
Text(0, 0.5, 'Price')
Insight 2 from Item Description:
- As the item description gets longer the price starts decreasing
Let us check what length of item description occurs the most
In [66]:
desc_len_count = data1.Description_length.value_counts()
print(desc_len_count[0:3])
3 12346 6 5503 7 5423 Name: Description_length, dtype: int64
In [67]:
plt.figure(figsize=(20, 10))
sns.barplot(desc_len_count.index[0:11], desc_len_count[0:11])
plt.title('Item Description having most frequent number of words',fontsize=20)
plt.xticks(rotation = 0,wrap = True,fontsize = 15)
plt.xlabel('Number of words',fontsize=15)
plt.ylabel('Frequency',fontsize=15)
plt.show()
Insight 3 from Item Description:
- This tells us that there are a lot of item descriptions having 3 words in it
- The item descriptions with shorter length are siginificantly higher than longer description lengths
Text Processing:¶
Most of the time, the first steps of an NLP project is to "tokenize" your documents, which main purpose is to normalize our texts. The three fundamental stages will usually include:
- break the descriptions into sentences and then break the sentences into tokens
- remove punctuation and stop words
- lowercase the tokens
Wordcloud for Item Description:¶
To check frequently occuring most important words in the description
In [68]:
from nltk.corpus import stopwords
stopwords = set(stopwords.words("english"))
from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
from PIL import Image
# checking length of stop words
len(stopwords)
from string import punctuation
punctuation = list(punctuation)
stopwords.update(punctuation)
# adding punctuation to stopwords
# wordcloud for Item Description variable with 500 most occuring words
doc1 = []
for i in range(0,data1.shape[0]):
text = str(data1["item_description"][i])
text = text.lower()
text = re.sub("[^a-zA-Z]", " ", text)
text = nltk.word_tokenize(text)
text = [lemmatizer.lemmatize(word) for word in text if word not in stopwords and len(word)>2]
text = " ".join(text)
doc1.append(text)
# join string
doc2 = "".join(doc1)
# wordcloud visualization
img = np.array(Image.open("../input/images/proj images/cmt.png"))
wordcloud = WordCloud(width=1000,height= 500,relative_scaling=1.0,mask=img,max_words=1000,
background_color='white',
stopwords=stopwords,
min_font_size=10).generate(doc2)
plt.figure(figsize=(15,10), facecolor=None)
plt.imshow(wordcloud)
plt.axis("off")
plt.show()
WordCloud for Item Description of Top 6 Categories¶
In [69]:
from nltk.corpus import stopwords
from PIL import Image
stp=set(stopwords.words('english'))
j=0
plt.figure(figsize=(8,10))
for i in data1["Main_categ"].value_counts().index[:6]:
text = data1.loc[data1["Main_categ"] == str(i)]["item_description"]
# text = data1.loc[data1['main_category']==str(i)]
# text = text['item_description']
text = "".join(text)
mask = np.array(Image.open('../input/images/proj images/'+str(i)+'.png'))
wordcloud = WordCloud(width=2000, height=1500, relative_scaling=1.0, max_words=100, mask=mask,
background_color='white',
stopwords=stp,
min_font_size=10,).generate(text)
# # With relative_scaling=1, a word that is twice as frequent will have twice the size.
# # If you want to consider the word frequencies and not only their rank, relative_scaling around .5 often looks good.
# # If ‘auto’ it will be set to 0.5 unless repeat is true, in which case it will be set to 0.
# # plot the WordCloud image
if j < 6:
j = j + 1
plt.subplot(3,2,j)
plt.imshow(wordcloud)
plt.axis("off")
plt.show()
Interactive Clustering graph with bokeh library:¶
Step 1- Pre-Processing: tf-idf¶
tf-idf is the acronym for Term Frequency–inverse Document Frequency. It quantifies the importance of a particular word in relative to the vocabulary of a collection of documents or corpus. The metric depends on two factors:
Term Frequency: the occurences of a word in a given document (i.e. bag of words) Inverse Document Frequency: the reciprocal number of times a word occurs in a corpus of documents Think about of it this way: If the word is used extensively in all documents, its existence within a specific document will not be able to provide us much specific information about the document itself. So the second term could be seen as a penalty term that penalizes common words such as "a", "the", "and", etc. tf-idf can therefore, be seen as a weighting scheme for words relevancy in a specific document.
In [70]:
data2=data1.copy()
In [71]:
from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
from nltk.corpus import stopwords
stp=set(stopwords.words('english'))
def tokenize(text):
text = str(text)
text = text.lower()
text = re.sub("[^a-zA-Z]", " ", text)
text = nltk.word_tokenize(text)
text = [lemmatizer.lemmatize(word) for word in text if word not in stp and len(word) > 2]
return text
data2['tokens'] = data2['item_description'].map(tokenize)
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(min_df=10,
max_features=180000,
tokenizer=tokenize,
)
data3 = data2.sample(n=15000)
vz = vectorizer.fit_transform(list(data3['item_description']))
df_idf = pd.DataFrame(vectorizer.idf_, index=vectorizer.get_feature_names(),columns=["tfidf"])
# Given the high dimension of our tfidf matrix, we need to reduce their dimension using the
# Singular Value Decomposition (SVD) technique. And to visualize our vocabulary, we could next use t-SNE to
# reduce the dimension from 50 to 2. t-SNE is more suitable for dimensionality reduction to 2 or 3.
from sklearn.decomposition import TruncatedSVD
n_comp=30
svd = TruncatedSVD(n_components=n_comp, random_state=0)
svd_tfidf = svd.fit_transform(vz)
from sklearn.manifold import TSNE
tsne_model = TSNE(n_components=2, random_state=0, n_iter=1000,perplexity=75)
tsne_tfidf= tsne_model.fit_transform(svd_tfidf)
Step 2- Singular Vector Decomposition:¶
- Given the high dimension of our tfidf matrix, we need to reduce their dimension using the Singular Value Decomposition (SVD) technique.
- And to visualize our vocabulary, we could next use t-SNE to reduce the dimension from 50 to 2.
- t-SNE is more suitable for dimensionality reduction to 2 or 3.
- In this case 2 coz we need to plot x-y axis
In [72]:
from sklearn.decomposition import TruncatedSVD
n_comp=30
svd = TruncatedSVD(n_components=n_comp, random_state=0)
svd_tfidf = svd.fit_transform(vz)
Step 3 - t Distributed Stochastic Neighbor Embedding (t-SNE)¶
- t-SNE is a technique for dimensionality reduction that is particularly well suited for the visualization of high-dimensional datasets.
- The goal is to take a set of points in a high-dimensional space and find a representation of those points in a lower-dimensional space, typically the 2D plane.
- It is based on probability distributions with random walk on neighborhood graphs to find the structure within the data. But since t-SNE complexity is significantly high, usually we'd use other high-dimension reduction techniques before applying t-SNE.
In [73]:
from sklearn.manifold import TSNE
tsne_model = TSNE(n_components=2, random_state=0, n_iter=1000,perplexity=75)
tsne_tfidf= tsne_model.fit_transform(svd_tfidf)
Step 4- K means Clustering:¶
- K-means clustering obejctive is to minimize the average squared Euclidean distance of the document / description from their cluster centroids.
In [74]:
from sklearn.cluster import KMeans
tfidf_df = pd.DataFrame(tsne_tfidf, columns=['x', 'y'])
temp = tfidf_df.copy()
model_kmeans = KMeans(n_clusters=10,random_state=10).fit(tfidf_df)
temp['description'] = data2['item_description']
temp['tokens'] = data2['tokens']
temp['category'] = data2['Main_categ']
temp['cluster'] = model_kmeans.predict(tfidf_df)
In [75]:
from bokeh.plotting import figure,show
from bokeh.io import output_file, output_notebook
from bokeh.transform import linear_cmap
from bokeh.palettes import Category10
from bokeh.resources import INLINE
import bokeh.io
bokeh.io.output_notebook(INLINE)
temp.to_csv("tfidf_tsne.csv")
sample = pd.read_csv("./tfidf_tsne.csv")
tooltip = [("Description","@description"),
("Tokens","@tokens"),
("Category","@category"),
("Cluster","@cluster")
]
mapper = linear_cmap(field_name='cluster', palette=Category10[10],low=0,high=9)
#output_notebook()
p= figure(plot_width=900, plot_height=900,
title="K-Means clustering of the item description",
tools="pan,wheel_zoom,box_zoom,reset,hover",
tooltips = tooltip)
p.scatter("x","y",source = sample, alpha=0.7,color = mapper)
# output_file("output_file_name.html")
# output_notebook()
# from IPython.display import IFrame
# IFrame(src='output_file_name.html', width=900, height=900)
show(p)
6. Item Condition:¶
In [76]:
plt.figure(figsize=(15, 10))
sns.boxplot(x=data1['item_condition_id'], y=np.log1p(data1.price))
plt.title('Box Plot of item condition id VS Product price',fontsize=20)
plt.xlabel('Item Condition ID',fontsize=15)
plt.ylabel('log(price+1)',fontsize=15)
plt.show()
In [77]:
data1.groupby(['item_condition_id'])['price'].agg('median')
Out[77]:
item_condition_id 1 18.0 2 18.0 3 16.0 4 15.0 5 19.0 Name: price, dtype: float64
Insight 1 from Item Condition:
- The median of Item condition as 5 is higher than the others which states that items with item condition 5 were more expensive than others
Item Condition For every Main Category¶
In [78]:
from collections import Counter
index = []
[index.append(key) for key, value in Counter(data1['Main_categ']).most_common()]
f,axes = plt.subplots(5, 2, figsize=(15, 30))
for i in range(data1['Main_categ'].nunique()):
sns.countplot(data1[data1['Main_categ'] == index[i]]['item_condition_id'], ax = axes[int(i/2)][i%2])
axes[int(i / 2)][i % 2].set_title(index[i],fontsize=15)
axes[int(i / 2)][i % 2].set_xlabel('Item Condition ID')
axes[int(i / 2)][i % 2].set_ylabel('Frequency')
Insight 2 for Item Condition:
- Item condition marked as 1, 2 and 3 are most frequent in
Women,Kids,MenandVintagecategory - Item condition marked as 4 and 5 are bought significantly less amount of times
BeautyandHandmadecategories have too many Item condition id as 1 as compared to other Item Condition ID- The most number of poor items which marked as 5 are found in the
Electronicscategory
Item Condition for Top 10 brands¶
In [79]:
from collections import Counter
temp=data1[data1['brand_name']!='Unknown']
index = []
[index.append(key) for key, value in Counter(temp['brand_name']).most_common()]
top_10 = index[:10]
temp2 = temp[temp['brand_name'].isin(top_10)]
f,axes = plt.subplots(5, 2, figsize=(15, 30))
for i in range(temp2['brand_name'].nunique()):
sns.countplot(temp2[temp2['brand_name'] == index[i]]['item_condition_id'], ax = axes[int(i/2)][i%2])
axes[int(i / 2)][i % 2].set_title(index[i],fontsize=15)
axes[int(i / 2)][i % 2].set_xlabel('Item Condition ID')
axes[int(i / 2)][i % 2].set_ylabel('Frequency')
Insight 3 from Item Condition ID:
- Brands like
Pink,NikeandMichael Korshave more number of New(1) to Good(3) condition items as compared to other brands American Eagledoesn't have a single product of poor quality(5)Applehas the most number of poor quality(5) products
Step 2 : Applying Machine Learning Models¶
Coming to our actual problem, we will be now applying machine learing models to give us a price that we should suggest to the seller. As discussed earlier, this is a regression problem, so we will be applying some regressions models based on our data and other factors that we will face during solving.
As we know that nothing is free in this world so we can say that price of data being zero is not possible. We observed that there are some rows which have price as 0. Hence we remove those rows
In [14]:
data2 = data1.loc[data1['price'] > 0]
In [15]:
data2.head()
Out[15]:
| train_id | name | item_condition_id | category_name | brand_name | price | shipping | item_description | Main_categ | sub_categ1 | sub_categ2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | Hero 77 fountain pen | 2 | Other/Office supplies/Writing | Unknown | 12.0 | 1 | For sale a brand new Hero 77 fountain pen, doe... | Other | Office supplies | Writing |
| 1 | 1 | 14K Yellow Gold Earrings | 3 | Women/Jewelry/Earrings | Unknown | 20.0 | 0 | 14k black Onyx earrings Good condition Final sale | Women | Jewelry | Earrings |
| 2 | 2 | New balance 2-in 1 size S dry fit shorts | 2 | Women/Athletic Apparel/Shorts | New Balance | 10.0 | 0 | Brand new never worn, but I tore the tag off w... | Women | Athletic Apparel | Shorts |
| 3 | 3 | Zella black workout tank w mesh cut out | 3 | Women/Athletic Apparel/Shirts & Tops | Zella | 15.0 | 1 | Zella black workout tank with mesh cut outs. | Women | Athletic Apparel | Shirts & Tops |
| 4 | 4 | NWT Lilly Pulitzer gabby dress sz 8 | 1 | Women/Dresses/Above Knee, Mini | Lilly Pulitzer | 75.0 | 0 | New with tags!! Size 8. | Women | Dresses | Above Knee, Mini |
Seperating dependent and independent variables
In [16]:
feature_cols = ['item_condition_id', 'Main_categ', 'sub_categ1','sub_categ2', 'brand_name', 'shipping']
x = data2[feature_cols] # Independent variables
y = np.log(data2['price']) # Dependent variable
Performing One hot encoding on categorical variables
In [17]:
cat_name = pd.get_dummies(data2["Main_categ"], drop_first=True) # drop_first=True means dropping the redundant column
brd_name = pd.get_dummies(data2["brand_name"], drop_first=True)
sub1_name = pd.get_dummies(data2["sub_categ1"], drop_first=True)
sub2_name = pd.get_dummies(data2["sub_categ2"], drop_first=True)
# removing the categorical columns
x.drop(['Main_categ', 'brand_name','sub_categ1', 'sub_categ2'], axis=1, inplace=True)
x1 = pd.concat([x, cat_name, brd_name, sub1_name, sub2_name], axis=1)
Spliting the data into train and test to validate the model
In [18]:
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x1, y, test_size=0.3, random_state=10)
1. OLS(Ordinary Least Squares Regression) Model:¶
In [85]:
from statsmodels.api import OLS
regr = OLS(y_train, x_train)
result = regr.fit()
print("Summary of OLS Model:-")
print(result.summary())
Summary of OLS Model:-
OLS Regression Results
=======================================================================================
Dep. Variable: price R-squared (uncentered): 0.961
Model: OLS Adj. R-squared (uncentered): 0.959
Method: Least Squares F-statistic: 865.9
Date: Tue, 07 Jul 2020 Prob (F-statistic): 0.00
Time: 13:45:43 Log-Likelihood: -94185.
No. Observations: 103718 AIC: 1.940e+05
Df Residuals: 100884 BIC: 2.211e+05
Df Model: 2834
Covariance Type: nonrobust
===========================================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------------------------------
item_condition_id -0.1318 0.002 -54.124 0.000 -0.137 -0.127
shipping -0.2623 0.004 -63.802 0.000 -0.270 -0.254
Electronics 0.9647 0.144 6.711 0.000 0.683 1.246
Handmade 0.2738 0.138 1.989 0.047 0.004 0.544
Home 0.0089 0.153 0.058 0.954 -0.292 0.309
Kids 0.5282 0.135 3.917 0.000 0.264 0.793
Men 0.5000 0.119 4.188 0.000 0.266 0.734
Other 0.3590 0.119 3.025 0.002 0.126 0.592
Sports & Outdoors 0.4876 0.181 2.689 0.007 0.132 0.843
Vintage & Collectibles 0.3347 0.138 2.431 0.015 0.065 0.605
Women 0.4213 0.119 3.551 0.000 0.189 0.654
3.1 Phillip Lim 3.3722 0.410 8.216 0.000 2.568 4.177
47 Brand 2.3560 0.327 7.206 0.000 1.715 2.997
5.11 Tactical 1.9138 0.645 2.969 0.003 0.650 3.177
5th & Ocean 1.62e-12 1.68e-13 9.660 0.000 1.29e-12 1.95e-12
7 For All Mankind® 2.3100 0.244 9.480 0.000 1.832 2.788
90 Degree By Reflex 1.3968 0.480 2.912 0.004 0.457 2.337
A Bathing Ape 3.1678 0.271 11.673 0.000 2.636 3.700
A Pea In The Pod 1.7103 0.352 4.863 0.000 1.021 2.400
A Plus Child Supply 1.5541 0.412 3.775 0.000 0.747 2.361
A Wish Come True 3.8593 0.653 5.909 0.000 2.579 5.139
A&R Sports 2.0985 0.645 3.256 0.001 0.835 3.362
A+D 1.1522 0.481 2.393 0.017 0.209 2.096
A. Byer 1.7303 0.411 4.215 0.000 0.926 2.535
A.K.A 1.5015 0.644 2.330 0.020 0.239 2.764
A/X Armani Exchange 2.7554 0.645 4.272 0.000 1.491 4.019
AA Aquarium 0.6270 0.648 0.968 0.333 -0.643 1.897
AB Studio 1.5184 0.371 4.088 0.000 0.790 2.247
ABC Studios 5.603e-14 1.17e-14 4.778 0.000 3.3e-14 7.9e-14
ABS by Allen Schwartz 1.0837 0.644 1.682 0.093 -0.179 2.347
ADAM 1.2217 0.482 2.534 0.011 0.277 2.167
AERIN -9.126e-13 9.4e-14 -9.711 0.000 -1.1e-12 -7.28e-13
AG Adriano Goldschmied 2.7451 0.327 8.391 0.000 2.104 3.386
AGB 1.9528 0.644 3.030 0.002 0.690 3.216
AKIRA 2.6246 0.484 5.427 0.000 1.677 3.572
AKOO 1.6408 0.650 2.525 0.012 0.367 2.914
ALDO 1.9595 0.237 8.285 0.000 1.496 2.423
ALEX AND ANI 2.4460 0.219 11.168 0.000 2.017 2.875
ALLOY 1.8209 0.644 2.825 0.005 0.558 3.084
ALO Yoga 2.9274 0.480 6.103 0.000 1.987 3.868
AMD 2.4513 0.379 6.475 0.000 1.709 3.193
AMIA 2.0223 0.645 3.137 0.002 0.759 3.286
AND1 1.1659 0.645 1.809 0.070 -0.097 2.429
ART 1.5944 0.304 5.250 0.000 0.999 2.190
ASICS 1.9291 0.235 8.201 0.000 1.468 2.390
ASOS 1.9205 0.243 7.889 0.000 1.443 2.398
ASTR 2.3716 0.644 3.680 0.000 1.108 3.635
ASUS 2.2769 0.279 8.161 0.000 1.730 2.824
AT-A-GLANCE 2.5795 0.416 6.195 0.000 1.763 3.396
Abbott 3.7239 0.649 5.738 0.000 2.452 4.996
Abercrombie & Fitch 1.8718 0.217 8.645 0.000 1.447 2.296
Abu Garcia 3.0772 0.654 4.704 0.000 1.795 4.359
Acacia Swimwear 3.4548 0.249 13.864 0.000 2.966 3.943
Academy 2.1740 0.645 3.373 0.001 0.911 3.437
Accessory Innovations 1.0848 0.649 1.671 0.095 -0.188 2.357
Accessory Workshop 1.7302 0.372 4.655 0.000 1.002 2.459
Acer 2.0851 0.349 5.977 0.000 1.401 2.769
Act 7.524e-13 7.18e-14 10.483 0.000 6.12e-13 8.93e-13
Active 1.1370 0.644 1.764 0.078 -0.126 2.400
Activision 2.4184 0.645 3.749 0.000 1.154 3.683
Adams Golf 1.7178 0.688 2.498 0.013 0.370 3.066
Adidas 2.1618 0.213 10.127 0.000 1.743 2.580
Adora 2.9782 0.645 4.618 0.000 1.714 4.242
Adrianna Papell 2.2602 0.371 6.087 0.000 1.532 2.988
Adrienne Landau 2.3380 0.480 4.869 0.000 1.397 3.279
Adrienne Vittadini 1.7915 0.480 3.732 0.000 0.851 2.733
Advanced Healthcare Distributors 2.5101 0.331 7.593 0.000 1.862 3.158
Advantage 2.5242 0.648 3.896 0.000 1.254 3.794
AeroPress 1.6593 0.484 3.431 0.001 0.711 2.607
Aeropostale 1.6395 0.218 7.528 0.000 1.213 2.066
Aerosoles 1.6976 0.345 4.918 0.000 1.021 2.374
Affliction 2.3419 0.236 9.926 0.000 1.879 2.804
Agent Provocateur 3.0350 0.644 4.709 0.000 1.772 4.298
Air Force 2.3044 0.346 6.669 0.000 1.627 2.982
Air Jordan 2.7453 0.216 12.712 0.000 2.322 3.169
Air Wick 1.8407 0.355 5.191 0.000 1.146 2.536
Airwalk 2.2109 0.654 3.380 0.001 0.929 3.493
Alan Flusser 1.9536 0.775 2.521 0.012 0.434 3.473
Albert Nipon 4.413e-13 5.52e-14 7.997 0.000 3.33e-13 5.49e-13
Alberto Makali 2.2782 0.644 3.535 0.000 1.015 3.541
Alegro 2.1790 0.679 3.207 0.001 0.847 3.511
Alex Toys 2.7334 0.484 5.651 0.000 1.785 3.681
Alexander Julian -2.484e-13 2.48e-14 -10.004 0.000 -2.97e-13 -2e-13
Alexander McQueen 3.5630 0.411 8.676 0.000 2.758 4.368
Alexander Wang 3.5330 0.480 7.364 0.000 2.593 4.473
Alexis Brittar 4.2458 0.644 6.588 0.000 2.983 5.509
Alfani 2.0164 0.314 6.430 0.000 1.402 2.631
Alfred Angelo 2.0769 0.482 4.310 0.000 1.132 3.021
Alfred Dunner 1.3120 0.648 2.026 0.043 0.043 2.581
Ali & Kris -2.754e-13 2.71e-14 -10.170 0.000 -3.29e-13 -2.22e-13
Alice + Olivia 2.5859 0.645 4.012 0.000 1.323 3.849
Alife 2.6043 0.645 4.036 0.000 1.340 3.869
All 2.2541 0.247 9.143 0.000 1.771 2.737
AllSaints 2.7305 0.424 6.441 0.000 1.900 3.561
Almay 1.6043 0.284 5.650 0.000 1.048 2.161
Almost Famous 1.5833 0.262 6.052 0.000 1.071 2.096
Alo 2.8997 0.327 8.877 0.000 2.259 3.540
Alpinestars 2.1302 0.645 3.304 0.001 0.866 3.394
Alstyle Apparel 1.5925 0.480 3.317 0.001 0.652 2.533
Altar'd State 2.4265 0.410 5.912 0.000 1.622 3.231
Altec Lansing 2.1604 0.414 5.217 0.000 1.349 2.972
Alternative 1.8939 0.480 3.948 0.000 0.954 2.834
Alternative Apparel 1.8116 0.644 2.812 0.005 0.549 3.074
Altra 1.2763 0.645 1.980 0.048 0.013 2.540
Always 2.0953 0.305 6.870 0.000 1.498 2.693
Amazon 1.8143 0.412 4.406 0.000 1.007 2.621
AmazonBasics 2.1460 0.236 9.086 0.000 1.683 2.609
Ambi 1.5553 0.644 2.413 0.016 0.292 2.818
Ambiance Apparel 1.7521 0.272 6.453 0.000 1.220 2.284
Ambrielle 1.7907 0.371 4.822 0.000 1.063 2.518
Ameda 2.3345 0.422 5.527 0.000 1.507 3.162
American Apparel 2.0678 0.220 9.381 0.000 1.636 2.500
American Boy & Girl 2.4022 0.219 10.977 0.000 1.973 2.831
American Eagle 1.8448 0.213 8.659 0.000 1.427 2.262
American Fighter 2.5076 0.287 8.737 0.000 1.945 3.070
American Girl ® 2.5261 0.221 11.444 0.000 2.093 2.959
American Rag 1.7108 0.259 6.615 0.000 1.204 2.218
Amsoil 2.449e-13 2.53e-14 9.664 0.000 1.95e-13 2.95e-13
Anastasia Beverly Hills 2.2136 0.218 10.168 0.000 1.787 2.640
Anchor Blue 1.2758 0.645 1.978 0.048 0.011 2.540
Anchor Hocking 1.5525 0.322 4.817 0.000 0.921 2.184
Andrea by Sadek 2.0268 0.734 2.761 0.006 0.588 3.465
Andrew Christian 1.6232 0.886 1.832 0.067 -0.114 3.360
Andrew Marc 2.6036 0.649 4.011 0.000 1.331 3.876
Angelcare 1.4308 0.675 2.118 0.034 0.107 2.755
Angelina 2.5667 0.644 3.983 0.000 1.304 3.830
Angels 1.3057 0.412 3.173 0.002 0.499 2.112
Angie 1.8484 0.411 4.495 0.000 1.042 2.654
Angry Birds 1.9875 0.645 3.081 0.002 0.723 3.252
Animal Planet 1.4694 0.645 2.278 0.023 0.205 2.733
Ann Taylor 1.7521 0.250 6.996 0.000 1.261 2.243
Ann Taylor LOFT 1.7166 0.223 7.704 0.000 1.280 2.153
Annabelle 1.6539 0.644 2.566 0.010 0.391 2.917
Anne Cole 1.9096 0.645 2.959 0.003 0.645 3.175
Anne Klein 2.0813 0.264 7.872 0.000 1.563 2.599
Anthropologie 2.3258 0.223 10.426 0.000 1.889 2.763
Antilia Femme 1.8210 0.480 3.795 0.000 0.880 2.762
Antique Rivet 1.6689 0.648 2.575 0.010 0.399 2.939
Antonio Melani 2.1260 0.345 6.160 0.000 1.450 2.802
Anvil 1.7289 0.376 4.602 0.000 0.993 2.465
Apostrophe 1.8901 0.645 2.932 0.003 0.626 3.154
Apple 2.5442 0.213 11.936 0.000 2.126 2.962
Apple Bottoms 3.1070 0.314 9.906 0.000 2.492 3.722
Apt. 1.6583 0.268 6.197 0.000 1.134 2.183
Apt. 9 1.4094 0.345 4.084 0.000 0.733 2.086
Aqua 2.2538 0.644 3.497 0.000 0.991 3.517
Aqua Lung Sport 2.2436 0.652 3.441 0.001 0.966 3.521
Aquaphor 2.2492 0.685 3.284 0.001 0.907 3.591
Aquatopia 1.7695 0.673 2.630 0.009 0.451 3.088
Aqueon 3.4417 0.648 5.313 0.000 2.172 4.711
Arbonne 2.3279 0.284 8.196 0.000 1.771 2.885
Arc'teryx 2.7086 0.372 7.284 0.000 1.980 3.437
Architect 1.3740 0.645 2.129 0.033 0.109 2.639
Ardell 1.8737 0.252 7.426 0.000 1.379 2.368
Arden B 2.2157 0.371 5.973 0.000 1.489 2.943
Ariat 2.5924 0.271 9.550 0.000 2.060 3.124
Aritzia 2.9908 0.481 6.215 0.000 2.048 3.934
Ariya -8.38e-14 1.14e-14 -7.337 0.000 -1.06e-13 -6.14e-14
Arizona 1.7338 0.261 6.633 0.000 1.221 2.246
Arizona Jean Company 1.5898 0.276 5.770 0.000 1.050 2.130
Ark & Co 1.7528 0.480 3.652 0.000 0.812 2.693
Arm & Hammer 0.6452 0.646 0.999 0.318 -0.620 1.911
Armani 3.8960 0.645 6.040 0.000 2.632 5.160
Armani Jeans 2.9156 0.482 6.053 0.000 1.972 3.860
Aroma 1.7529 0.373 4.697 0.000 1.021 2.484
Arrow 1.6917 0.656 2.578 0.010 0.406 2.978
Artful Dodger -3.422e-14 9.03e-15 -3.788 0.000 -5.19e-14 -1.65e-14
As Seen on TV 2.6044 0.645 4.037 0.000 1.340 3.869
Ashley B 1.9064 0.645 2.958 0.003 0.643 3.170
Ashley Stewart 1.7593 0.313 5.613 0.000 1.145 2.374
Aspen Pet 2.6720 0.646 4.135 0.000 1.405 3.939
Athleta 2.0651 0.231 8.951 0.000 1.613 2.517
Athletech 1.0786 0.646 1.671 0.095 -0.187 2.344
Atmosphere 1.5746 0.645 2.441 0.015 0.310 2.839
Attention 0.5109 0.644 0.793 0.428 -0.752 1.774
Audi 2.0551 0.645 3.186 0.001 0.791 3.319
Audio-Technica 4.3276 0.646 6.702 0.000 3.062 5.593
AudioSource 0.6448 0.647 0.997 0.319 -0.623 1.912
August Silk 2.1297 0.480 4.435 0.000 1.189 3.071
Augusta Sportswear 1.8736 0.646 2.902 0.004 0.608 3.139
Aurora World 1.9216 0.412 4.669 0.000 1.115 2.728
Authentic Original Vintage Style 1.273e-13 1.45e-14 8.810 0.000 9.9e-14 1.56e-13
Aveda 2.1088 0.259 8.151 0.000 1.602 2.616
Aveeno 2.5071 0.389 6.446 0.000 1.745 3.269
Avent 1.7787 0.289 6.150 0.000 1.212 2.346
Avenue 2.0158 0.328 6.150 0.000 1.373 2.658
Avery 1.9736 0.484 4.079 0.000 1.025 2.922
Avia 1.4626 0.327 4.476 0.000 0.822 2.103
Avirex 1.7432 0.645 2.704 0.007 0.480 3.007
Avon 1.7454 0.224 7.792 0.000 1.306 2.184
Axe 1.9848 0.412 4.812 0.000 1.176 2.793
A|X Armani Exchange 2.1719 0.259 8.387 0.000 1.664 2.679
Aéropostale 1.6051 0.249 6.453 0.000 1.118 2.093
B. Darlin 2.3427 0.371 6.311 0.000 1.615 3.070
B.swim 1.7618 0.645 2.733 0.006 0.498 3.025
BB 2.8816 0.308 9.352 0.000 2.278 3.485
BB Dakota 1.9362 0.411 4.714 0.000 1.131 2.741
BCBG 1.7509 0.371 4.719 0.000 1.024 2.478
BCBGMAXAZRIA 2.1970 0.244 9.019 0.000 1.720 2.674
BCBGeneration 1.9897 0.250 7.944 0.000 1.499 2.481
BCX 1.6090 0.483 3.328 0.001 0.661 2.556
BDG 2.0078 0.267 7.506 0.000 1.484 2.532
BEARPAW 1.7917 0.645 2.780 0.005 0.528 3.055
BISOU BISOU 1.6603 0.411 4.040 0.000 0.855 2.466
BKE 2.2424 0.272 8.241 0.000 1.709 2.776
BLANKNYC 1.3859 0.645 2.147 0.032 0.121 2.651
BMW 1.9914 0.646 3.081 0.002 0.725 3.258
BOSS HUGO BOSS 2.3945 0.371 6.449 0.000 1.667 3.122
BOYS + ARROWS 3.9478 0.645 6.125 0.000 2.684 5.211
BUNN 2.4793 0.657 3.772 0.000 1.191 3.768
Babies R Us 1.9581 0.341 5.737 0.000 1.289 2.627
Babies R Us Plush 1.5317 0.822 1.863 0.062 -0.080 3.143
Babolat -1.669e-13 1.97e-14 -8.491 0.000 -2.05e-13 -1.28e-13
Baby Brezza 3.8202 0.489 7.819 0.000 2.863 4.778
Baby Bullet 6.743e-13 7.25e-14 9.297 0.000 5.32e-13 8.16e-13
Baby Einstein 2.0482 0.376 5.440 0.000 1.310 2.786
Baby Jogger 3.0525 0.525 5.810 0.000 2.023 4.082
Baby Phat 1.7706 0.327 5.415 0.000 1.130 2.411
Baby Trend 2.3996 0.481 4.993 0.000 1.458 3.342
BabyBjorn 2.0259 0.374 5.423 0.000 1.294 2.758
BabyGanics 0.9966 0.655 1.521 0.128 -0.287 2.281
Bachmann Trains 1.6612 0.645 2.576 0.010 0.397 2.925
Badgley Mischka 2.9833 0.411 7.260 0.000 2.178 3.789
Baggallini 1.7279 0.645 2.680 0.007 0.464 2.992
Bailey -3.705e-13 3.32e-14 -11.166 0.000 -4.35e-13 -3.05e-13
Bailey 44 1.7892 0.644 2.777 0.005 0.526 3.052
Baker by Ted Baker -4.074e-13 4.46e-14 -9.129 0.000 -4.95e-13 -3.2e-13
Bakers 1.7742 0.645 2.751 0.006 0.510 3.038
Balboa Baby 2.4395 0.680 3.588 0.000 1.107 3.772
Balenciaga 3.7039 0.411 9.019 0.000 2.899 4.509
Ball 2.0293 0.411 4.940 0.000 1.224 2.834
Bally 1.2968 0.644 2.012 0.044 0.034 2.560
Balmain 2.9545 0.345 8.555 0.000 2.278 3.631
Bamboobies 1.4804 0.655 2.261 0.024 0.197 2.764
Banana Republic 1.8386 0.223 8.249 0.000 1.402 2.275
Bandai 2.5257 0.281 8.980 0.000 1.974 3.077
Bandolino 1.8262 0.345 5.287 0.000 1.149 2.503
Banzai 1.4556 0.657 2.214 0.027 0.167 2.744
Bape 2.8653 0.247 11.582 0.000 2.380 3.350
Baratza 3.621e-13 5.27e-14 6.875 0.000 2.59e-13 4.65e-13
Barbasol 1.3383 0.646 2.073 0.038 0.073 2.604
Barbie 1.9255 0.224 8.587 0.000 1.486 2.365
Bare Escentuals 2.0334 0.233 8.712 0.000 1.576 2.491
Bare Minerals 2.0148 0.645 3.122 0.002 0.750 3.280
Basic Editions 1.3583 0.644 2.108 0.035 0.095 2.621
Bass 2.0424 0.313 6.521 0.000 1.428 2.656
Bath & Body Works 1.9182 0.213 8.985 0.000 1.500 2.337
Batman 1.5258 0.480 3.178 0.001 0.585 2.467
Bauer -4.379e-13 4.71e-14 -9.294 0.000 -5.3e-13 -3.46e-13
Bausch & Lomb 1.2854 0.412 3.116 0.002 0.477 2.094
Bay Island 2.1613 0.480 4.502 0.000 1.220 3.102
Bay Studio 0.9456 0.645 1.467 0.142 -0.318 2.209
BeBop 1.5260 0.371 4.113 0.000 0.799 2.253
Beaba -1.351e-13 1.3e-14 -10.413 0.000 -1.61e-13 -1.1e-13
Beach Bunny 3.9384 0.313 12.564 0.000 3.324 4.553
Beats 3.5553 0.224 15.861 0.000 3.116 3.995
Beats by Dr. Dre 3.4735 0.227 15.320 0.000 3.029 3.918
BeautiControl 2.3429 0.482 4.858 0.000 1.398 3.288
Bebe 2.2212 0.218 10.191 0.000 1.794 2.648
Bebe Au Lait 1.8745 0.494 3.797 0.000 0.907 2.842
Becca 2.4570 0.345 7.116 0.000 1.780 3.134
Becca Cosmetics 2.2682 0.480 4.727 0.000 1.328 3.209
Bed Stu -5.641e-13 6.63e-14 -8.504 0.000 -6.94e-13 -4.34e-13
Beechnut 1.8965 0.489 3.881 0.000 0.939 2.854
Belkin 1.3650 0.480 2.842 0.004 0.424 2.306
Bella 2.3196 0.371 6.254 0.000 1.593 3.047
Bella Cucina 1.6117 0.487 3.307 0.001 0.656 2.567
Belle by Sigerson Morrison 5.835e-13 6.8e-14 8.577 0.000 4.5e-13 7.17e-13
Belly Bandit® 2.4060 0.329 7.314 0.000 1.761 3.051
Ben Nye 1.7077 0.264 6.469 0.000 1.190 2.225
Ben Sherman 1.6779 0.484 3.470 0.001 0.730 2.626
Bench 2.2358 0.480 4.657 0.000 1.295 3.177
Benefit 2.1064 0.217 9.706 0.000 1.681 2.532
Benetton -4.616e-13 4.91e-14 -9.409 0.000 -5.58e-13 -3.65e-13
Berkley 0.9718 0.654 1.486 0.137 -0.310 2.254
Bernardo 1.8207 0.648 2.810 0.005 0.551 3.091
Betsey Johnson 2.0140 0.218 9.258 0.000 1.588 2.440
Betsey Johnson Label 1.4688 0.645 2.277 0.023 0.204 2.733
Betsy & Adam 2.6100 0.645 4.049 0.000 1.347 3.873
Bettie Page -1.88e-13 3.17e-14 -5.933 0.000 -2.5e-13 -1.26e-13
Betty Boop 1.5246 0.314 4.858 0.000 0.910 2.140
Beverly Hills Polo Club 2.1068 0.483 4.363 0.000 1.160 3.053
Beyond Yoga 2.2619 0.371 6.099 0.000 1.535 2.989
Bh 3.562e-13 4.41e-14 8.070 0.000 2.7e-13 4.43e-13
Bic 1.6147 0.283 5.705 0.000 1.060 2.169
Big Buddha 1.3451 0.645 2.087 0.037 0.082 2.608
Big Star 2.3447 0.251 9.328 0.000 1.852 2.837
Bike 1.4224 0.646 2.204 0.028 0.157 2.688
Bikerwear 1.0859 0.646 1.682 0.093 -0.179 2.351
Billabong 1.6994 0.237 7.157 0.000 1.234 2.165
Billionaire Boys Club 2.7687 0.645 4.295 0.000 1.505 4.032
Billionaire Boys Club and Ice Cream 2.3925 0.645 3.708 0.000 1.128 3.657
Billy Reid 2.1430 0.653 3.280 0.001 0.862 3.424
Biore 1.3040 0.644 2.023 0.043 0.041 2.567
Bioworld 1.9975 0.372 5.377 0.000 1.269 2.726
Birkenstock 2.7432 0.230 11.931 0.000 2.293 3.194
Bissell 2.9827 0.646 4.615 0.000 1.716 4.249
Black & Decker 1.5098 0.349 4.328 0.000 0.826 2.194
Black Rivet 2.4771 0.645 3.843 0.000 1.214 3.740
Blackmilk 2.6464 0.644 4.107 0.000 1.384 3.909
Blair -2.265e-13 2.93e-14 -7.727 0.000 -2.84e-13 -1.69e-13
Blanc Noir -2.48e-13 3.31e-14 -7.493 0.000 -3.13e-13 -1.83e-13
Blendtec 4.2374 0.421 10.055 0.000 3.411 5.063
Blinc 1.6522 0.480 3.444 0.001 0.712 2.593
Blip Toys -8.471e-13 1.08e-13 -7.864 0.000 -1.06e-12 -6.36e-13
Blizzard 1.7323 0.346 5.011 0.000 1.055 2.410
Blooming Bath 2.2956 0.673 3.412 0.001 0.977 3.614
Bloomingdale's 2.8773 0.649 4.432 0.000 1.605 4.150
Blowfish 1.7297 0.480 3.605 0.000 0.789 2.670
Blue Asphalt 1.6145 0.647 2.495 0.013 0.346 2.883
Blue Buffalo 3.3933 0.648 5.238 0.000 2.124 4.663
Blue Life 2.8654 0.411 6.970 0.000 2.060 3.671
Blue Microphones 2.4671 0.463 5.325 0.000 1.559 3.375
Blunt Power 2.2666 0.646 3.510 0.000 1.001 3.532
Bob Mackie 1.5129 0.646 2.343 0.019 0.247 2.779
Bobbi Brown 2.4738 0.249 9.950 0.000 1.986 2.961
Bobbie Brooks 1.3699 0.302 4.529 0.000 0.777 1.963
Bobeau 1.1705 0.645 1.816 0.069 -0.093 2.434
Boden 1.6643 0.668 2.491 0.013 0.355 2.974
Bodum 1.7799 0.411 4.327 0.000 0.974 2.586
Body & Hair 1.4659 0.645 2.273 0.023 0.202 2.730
Body Central 1.5987 0.411 3.886 0.000 0.792 2.405
Body Glove 1.3721 0.411 3.336 0.001 0.566 2.178
Boho Chic 1.8235 0.646 2.823 0.005 0.558 3.089
Bongo 1.6467 0.256 6.426 0.000 1.144 2.149
Bonne Bell 1.5471 0.249 6.214 0.000 1.059 2.035
Boo! 1.5788 0.412 3.836 0.000 0.772 2.386
Boogie Wipes 4.124e-14 1.08e-14 3.818 0.000 2.01e-14 6.24e-14
Boohoo 1.6705 0.287 5.830 0.000 1.109 2.232
Boohoo Plus 1.7621 0.480 3.673 0.000 0.822 2.702
Boom Boom 1.2017 0.480 2.504 0.012 0.261 2.142
Boon 2.0326 0.489 4.160 0.000 1.075 2.990
Boots 1.6604 0.480 3.459 0.001 0.720 2.601
Boppy 1.9888 0.293 6.790 0.000 1.415 2.563
Borghese -5.756e-14 1.05e-14 -5.500 0.000 -7.81e-14 -3.71e-14
Born 2.1726 0.313 6.937 0.000 1.559 2.786
BornFree 2.1863 0.651 3.359 0.001 0.910 3.462
Bose 3.6872 0.254 14.522 0.000 3.190 4.185
Boston Proper 2.1682 0.645 3.362 0.001 0.904 3.432
Bottega Veneta 4.1170 0.647 6.363 0.000 2.849 5.385
Boudreaux -5.497e-13 6.79e-14 -8.094 0.000 -6.83e-13 -4.17e-13
Bourbon and Bowties 2.1394 0.645 3.319 0.001 0.876 3.403
Bourjois 2.1238 0.644 3.296 0.001 0.861 3.387
Brahmin 4.1037 0.645 6.367 0.000 2.840 5.367
Brandy Melville 2.0321 0.214 9.495 0.000 1.613 2.452
Braun 3.6717 0.402 9.142 0.000 2.884 4.459
Bravado 1.8858 0.281 6.722 0.000 1.336 2.436
Bravado Designs 1.8239 0.655 2.786 0.005 0.541 3.107
Brave Soul 2.2275 0.648 3.437 0.001 0.957 3.498
BreathableBaby 1.6685 0.485 3.439 0.001 0.718 2.620
Breckelles 2.0156 0.480 4.200 0.000 1.075 2.956
Breitling -6.65e-13 8.74e-14 -7.605 0.000 -8.36e-13 -4.94e-13
Brentwood 1.5183 0.645 2.354 0.019 0.254 2.782
Breville 3.5696 0.645 5.535 0.000 2.305 4.834
Breyer 2.5744 0.351 7.331 0.000 1.886 3.263
Briggs New York 1.4615 0.646 2.264 0.024 0.196 2.727
Bright Starts 2.0204 0.378 5.341 0.000 1.279 2.762
Brighton 2.4836 0.231 10.763 0.000 2.031 2.936
Brine 1.5711 0.654 2.404 0.016 0.290 2.852
Brita 1.4709 0.542 2.715 0.007 0.409 2.533
Britax 2.8714 0.439 6.542 0.000 2.011 3.732
Brixton -3.355e-13 4.64e-14 -7.224 0.000 -4.27e-13 -2.44e-13
Brooklyn Express 1.6114 0.645 2.500 0.012 0.348 2.875
Brooks 1.9370 0.272 7.133 0.000 1.405 2.469
Brooks Brothers 2.1331 0.297 7.175 0.000 1.550 2.716
Brookstone 1.3528 0.650 2.081 0.037 0.079 2.627
Brother 3.3135 0.648 5.114 0.000 2.044 4.583
Browning 1.2868 0.372 3.461 0.001 0.558 2.016
Bruder Toys America 2.0732 0.648 3.198 0.001 0.802 3.344
Buckle 2.2339 0.220 10.172 0.000 1.803 2.664
Buffalo -4.425e-13 6.11e-14 -7.247 0.000 -5.62e-13 -3.23e-13
Buffalo David Bitton 1.1700 0.645 1.815 0.070 -0.094 2.434
Bugaboo 2.9645 0.350 8.472 0.000 2.279 3.650
Bugle Boy 0.9567 0.645 1.484 0.138 -0.307 2.221
Bullhead 1.7602 0.234 7.512 0.000 1.301 2.220
Bumble Collection 1.984e-13 2.57e-14 7.725 0.000 1.48e-13 2.49e-13
Bumbo 1.8516 0.354 5.224 0.000 1.157 2.546
BumpStart 2.2085 0.650 3.398 0.001 0.935 3.482
Burberry 3.1077 0.226 13.758 0.000 2.665 3.550
Burberry Brit 3.5490 0.294 12.075 0.000 2.973 4.125
Burberry London 3.9254 0.645 6.090 0.000 2.662 5.189
Burt's Bees 1.4429 0.261 5.526 0.000 0.931 1.955
Burt's Bees Baby 1.9792 0.522 3.789 0.000 0.955 3.003
Burton 2.4835 0.314 7.898 0.000 1.867 3.100
Bushnell 2.1554 0.647 3.333 0.001 0.888 3.423
Buxom 1.4719 0.480 3.068 0.002 0.532 2.412
Bvlgari 3.4628 0.414 8.374 0.000 2.652 4.273
Byer Too 1.0932 0.645 1.696 0.090 -0.170 2.356
C by Champion 1.9068 0.644 2.959 0.003 0.644 3.170
CALIA by Carrie Underwood 2.5769 0.485 5.316 0.000 1.627 3.527
CAbi 1.8444 0.345 5.345 0.000 1.168 2.521
CB Sports 1.8344 0.715 2.565 0.010 0.433 3.236
CBK 2.2916 0.652 3.513 0.000 1.013 3.570
CCB Collection 1.3272 0.645 2.058 0.040 0.063 2.591
CCM 2.0755 0.414 5.008 0.000 1.263 2.888
CHI -5.186e-14 1.36e-14 -3.808 0.000 -7.85e-14 -2.52e-14
CJ Banks 4.928e-14 1.03e-14 4.773 0.000 2.9e-14 6.95e-14
CLEAN 1.2822 0.645 1.988 0.047 0.018 2.546
COOGI 2.8346 0.374 7.577 0.000 2.101 3.568
CW-X 2.6760 0.480 5.579 0.000 1.736 3.616
Cabela's 1.8647 0.371 5.026 0.000 1.137 2.592
Cable & Gauge 2.0941 0.411 5.101 0.000 1.290 2.899
Cache 2.1115 0.328 6.435 0.000 1.468 2.755
Cacique 2.1575 0.238 9.056 0.000 1.691 2.624
Cali 1.5584 0.480 3.247 0.001 0.618 2.499
Calico Critters 2.2134 0.304 7.290 0.000 1.618 2.809
Call It Spring 2.3962 0.645 3.716 0.000 1.132 3.660
Callaway 3.4656 0.688 5.039 0.000 2.118 4.814
Calphalon 1.094e-13 1.66e-14 6.606 0.000 7.7e-14 1.42e-13
Calvin Klein 2.1234 0.216 9.816 0.000 1.699 2.547
Cambridge Classics 2.312e-14 1.02e-14 2.267 0.023 3.13e-15 4.31e-14
CamelBak 2.0519 0.347 5.916 0.000 1.372 2.732
Canada Goose 3.1588 0.655 4.823 0.000 1.875 4.442
Candie's 1.6583 0.238 6.970 0.000 1.192 2.125
Canon 2.7646 0.237 11.682 0.000 2.301 3.228
Canvas 1.9097 0.411 4.652 0.000 1.105 2.714
Canyon River Blues 3.527e-14 1.01e-14 3.481 0.001 1.54e-14 5.51e-14
Capcom 2.9598 0.645 4.589 0.000 1.696 4.224
Capezio 1.8407 0.315 5.850 0.000 1.224 2.457
Carbon 1.9386 0.346 5.605 0.000 1.261 2.616
Cardinal Industries 1.7925 0.516 3.473 0.001 0.781 2.804
Cards Against Humanity -4.468e-14 8.83e-15 -5.062 0.000 -6.2e-14 -2.74e-14
Caress Brand Shop 2.5941 0.646 4.018 0.000 1.329 3.860
Carhartt 2.0698 0.244 8.491 0.000 1.592 2.548
Caribbean Joe 1.8347 0.411 4.468 0.000 1.030 2.640
Carlos Santana 1.5888 0.480 3.310 0.001 0.648 2.529
Carole Little 1.6348 0.644 2.537 0.011 0.372 2.898
Carolyn Taylor 1.3142 0.644 2.039 0.041 0.051 2.577
Carrera -4.494e-14 8.93e-15 -5.030 0.000 -6.24e-14 -2.74e-14
Carson Home Accents 1.4069 0.481 2.924 0.003 0.464 2.350
Carter's 1.8668 0.214 8.707 0.000 1.447 2.287
Cartier 3.1037 0.287 10.830 0.000 2.542 3.665
Cartoon Network 2.1490 0.313 6.856 0.000 1.535 2.763
Cascade 3.1518 0.886 3.557 0.000 1.415 4.889
Casio 2.2814 0.315 7.253 0.000 1.665 2.898
Caslon 1.8997 0.647 2.934 0.003 0.631 3.169
Casual Corner 1.5927 0.666 2.391 0.017 0.287 2.898
Catalina 1.5950 0.303 5.266 0.000 1.001 2.189
Caterpillar 9.206e-15 7.31e-15 1.259 0.208 -5.13e-15 2.35e-14
Catherine Malandrino 2.1804 0.645 3.383 0.001 0.917 3.444
Catherines 2.4903 0.645 3.858 0.000 1.225 3.755
Cato 1.5313 0.246 6.224 0.000 1.049 2.013
Celebrity Pink 1.4655 0.327 4.482 0.000 0.825 2.106
Celine 3.5719 0.294 12.156 0.000 2.996 4.148
Cello Jeans 1.6815 0.411 4.091 0.000 0.876 2.487
Century -1.213e-14 8.7e-15 -1.395 0.163 -2.92e-14 4.92e-15
Cezani 1.6930 0.656 2.580 0.010 0.407 2.979
Chaco 3.0014 0.244 12.281 0.000 2.522 3.480
Chadwicks 1.8211 0.487 3.739 0.000 0.866 2.776
Chalk Line 3.3360 0.645 5.173 0.000 2.072 4.600
Champion 1.6913 0.221 7.653 0.000 1.258 2.124
Champion Sports 1.8601 0.645 2.885 0.004 0.596 3.124
Champs 2.1598 0.654 3.302 0.001 0.878 3.442
Chanel 2.9648 0.217 13.648 0.000 2.539 3.391
Channel 2.8482 0.645 4.419 0.000 1.585 4.112
Chantelle 2.2410 0.480 4.670 0.000 1.300 3.182
Chaps 1.6662 0.272 6.130 0.000 1.133 2.199
Charles River Apparel 2.1274 0.645 3.299 0.001 0.864 3.391
Charlotte Russe 1.5943 0.215 7.409 0.000 1.173 2.016
Charlotte Tilbury 2.8995 0.345 8.404 0.000 2.223 3.576
Charming Charlie 1.4447 0.280 5.151 0.000 0.895 1.994
Charter Club 1.7917 0.371 4.829 0.000 1.065 2.519
Chaser 1.8629 0.480 3.882 0.000 0.922 2.803
Chef's Secret 2.483e-15 8.96e-15 0.277 0.782 -1.51e-14 2e-14
Cherokee 1.9111 0.245 7.806 0.000 1.431 2.391
Chesley 1.4526 0.644 2.254 0.024 0.189 2.716
Chicco 2.5271 0.294 8.591 0.000 1.951 3.104
Chico's 2.0338 0.264 7.702 0.000 1.516 2.551
Children's Place 1.8221 0.229 7.968 0.000 1.374 2.270
Chinese Laundry 1.9734 0.346 5.708 0.000 1.296 2.651
Chloe 2.7685 0.345 8.018 0.000 2.092 3.445
Chloe + Isabel 3.3291 0.644 5.166 0.000 2.066 4.592
Chloe K 4.341e-16 9.2e-15 0.047 0.962 -1.76e-14 1.85e-14
Chloé 3.7854 0.645 5.870 0.000 2.521 5.049
Christian Audigier 2.553e-15 7.21e-15 0.354 0.723 -1.16e-14 1.67e-14
Christian Dior 2.6116 0.226 11.559 0.000 2.169 3.054
Christian Lacroix 1.6929 0.645 2.626 0.009 0.430 2.956
Christian Louboutin 4.0007 0.245 16.304 0.000 3.520 4.482
Christian Siriano 1.8048 0.371 4.861 0.000 1.077 2.533
Christophe Robin 1.3142 0.647 2.030 0.042 0.045 2.583
Christopher & Banks 2.0738 0.646 3.211 0.001 0.808 3.339
Chroma⢠-1.171e-14 6.87e-15 -1.704 0.088 -2.52e-14 1.76e-15
Chrome Hearts 0.7141 0.645 1.107 0.268 -0.550 1.978
Ciaté London 1.8588 0.410 4.530 0.000 1.055 2.663
Cinch 2.5939 0.481 5.398 0.000 1.652 3.536
Circo 5.768e-15 8.44e-15 0.684 0.494 -1.08e-14 2.23e-14
Circus by Sam Edelman 1.9285 0.645 2.991 0.003 0.665 3.192
Citizen 1.5639 0.645 2.425 0.015 0.300 2.828
Citizens of Humanity 1.9304 0.328 5.893 0.000 1.288 2.572
Citizens of Humanity Jeans 1.5617 0.650 2.404 0.016 0.289 2.835
City Chic -1.58e-15 1.11e-14 -0.143 0.886 -2.33e-14 2.01e-14
City Streets 1.4131 0.410 3.443 0.001 0.609 2.218
City Triangles 1.2057 0.645 1.869 0.062 -0.059 2.470
Claiborne 1.5809 0.484 3.265 0.001 0.632 2.530
Claire's 1.1760 0.286 4.106 0.000 0.615 1.737
Clarins 2.0853 0.313 6.658 0.000 1.471 2.699
Clarisonic -1.554e-15 8.3e-15 -0.187 0.851 -1.78e-14 1.47e-14
Clarks 1.8444 0.413 4.470 0.000 1.036 2.653
Cleveland Golf 1.6045 0.688 2.333 0.020 0.256 2.953
Clinique 1.9641 0.219 8.971 0.000 1.535 2.393
Clinique Du Rond -5.994e-15 7.3e-15 -0.821 0.411 -2.03e-14 8.31e-15
Cloud B -1.123e-14 7.5e-15 -1.498 0.134 -2.59e-14 3.46e-15
Club Monaco 1.3023 0.645 2.020 0.043 0.039 2.566
CoCaLo -1.043e-15 7.52e-15 -0.139 0.890 -1.58e-14 1.37e-14
Coach 2.4681 0.213 11.564 0.000 2.050 2.886
Coast 2.1555 0.647 3.333 0.001 0.888 3.423
Cobra Electronics -8.325e-15 6.33e-15 -1.316 0.188 -2.07e-14 4.08e-15
Coca-Cola 1.9136 0.480 3.986 0.000 0.973 2.855
Coco Reef -4.375e-15 6.61e-15 -0.661 0.508 -1.73e-14 8.59e-15
Coldwater Creek 1.8523 0.480 3.858 0.000 0.911 2.793
Cole Haan 2.2719 0.250 9.073 0.000 1.781 2.763
Coleman 1.2665 0.482 2.630 0.009 0.323 2.210
Colgate 1.7869 0.278 6.434 0.000 1.243 2.331
Colin Stuart -2.021e-14 1.05e-14 -1.921 0.055 -4.08e-14 4.05e-16
Colosseum 2.0762 0.645 3.221 0.001 0.813 3.340
ColourPop Cosmetics 2.1230 0.223 9.506 0.000 1.685 2.561
Columbia 1.9128 0.218 8.757 0.000 1.485 2.341
Comfort & Harmony -4.723e-15 7.41e-15 -0.637 0.524 -1.93e-14 9.81e-15
Comfort Colors 2.0370 0.249 8.189 0.000 1.549 2.525
Comme des Garcons 3.1832 0.480 6.631 0.000 2.242 4.124
Compaq 1.072e-14 7.56e-15 1.418 0.156 -4.09e-15 2.55e-14
Conair 1.7305 0.413 4.194 0.000 0.922 2.539
Connected 1.3555 0.645 2.103 0.035 0.092 2.619
Converse 1.8448 0.216 8.524 0.000 1.421 2.269
Converse Shoes 1.5858 0.276 5.742 0.000 1.045 2.127
Cooks Essentials 1.7205 0.654 2.631 0.009 0.439 3.002
Cooper 2.1507 0.646 3.332 0.001 0.885 3.416
Cooperative 2.0750 0.480 4.324 0.000 1.134 3.016
Cordani Shoes 1.3688 0.645 2.124 0.034 0.105 2.632
Corelle 1.8081 0.481 3.763 0.000 0.866 2.750
CorningWare 2.0667 0.330 6.263 0.000 1.420 2.713
Corral 7.647e-15 6.54e-15 1.170 0.242 -5.16e-15 2.05e-14
Corso Como 2.238e-15 7.44e-15 0.301 0.764 -1.24e-14 1.68e-14
Cosabella 1.7343 0.647 2.682 0.007 0.467 3.002
Cosco 1.2994 0.412 3.156 0.002 0.492 2.106
Costa Del Mar 2.0267 0.480 4.225 0.000 1.086 2.967
Cotton On 1.6918 0.245 6.913 0.000 1.212 2.171
Cottonelle 1.9302 0.498 3.878 0.000 0.955 2.906
Coty 1.6871 0.327 5.166 0.000 1.047 2.327
Coty, Inc. 1.6988 0.644 2.636 0.008 0.436 2.962
Counterparts 1.5434 0.647 2.384 0.017 0.274 2.812
Cover Fx 2.4374 0.645 3.780 0.000 1.173 3.701
Cover Girl -9.691e-15 8.12e-15 -1.193 0.233 -2.56e-14 6.23e-15
CoverGirl 1.4878 0.228 6.518 0.000 1.040 1.935
Covington 1.9282 0.412 4.676 0.000 1.120 2.736
Cowgirl Tuff 2.5610 0.646 3.966 0.000 1.295 3.827
Cra-Z-Art -4.387e-15 9.03e-15 -0.486 0.627 -2.21e-14 1.33e-14
Craftsman 2.3239 0.680 3.420 0.001 0.992 3.656
Crayola 2.2312 0.328 6.808 0.000 1.589 2.874
Creative Designs 1.9017 0.645 2.948 0.003 0.637 3.166
Creativity for Kids 1.4956 0.481 3.112 0.002 0.554 2.438
Crest 2.3134 0.243 9.529 0.000 1.838 2.789
CritterWare 1.4773 0.648 2.281 0.023 0.208 2.747
Crocs 1.6842 0.223 7.536 0.000 1.246 2.122
Croft & Barrow 1.7347 0.282 6.148 0.000 1.182 2.288
Crooks & Castles 1.3474 0.645 2.090 0.037 0.084 2.611
Crosman 2.4800 0.653 3.797 0.000 1.200 3.760
Cruel Girl 1.6436 0.645 2.548 0.011 0.379 2.908
Cruiser Accessories 2.3385 0.649 3.605 0.000 1.067 3.610
Cuddl Duds -6.824e-15 9.61e-15 -0.710 0.477 -2.57e-14 1.2e-14
Cuisinart 1.9148 0.350 5.470 0.000 1.229 2.601
Current/Elliott 1.9135 0.480 3.986 0.000 0.973 2.854
Curvy Couture 2.18e-15 8.29e-15 0.263 0.793 -1.41e-14 1.84e-14
Custom Accessories 2.7740 0.415 6.686 0.000 1.961 3.587
Customized & Personalized 2.3337 0.218 10.694 0.000 1.906 2.761
Cutter & Buck 2.1921 0.481 4.561 0.000 1.250 3.134
Cutters -2.774e-15 7.75e-15 -0.358 0.720 -1.8e-14 1.24e-14
Cynthia Rowley 1.8828 0.313 6.018 0.000 1.270 2.496
Céline 3.3141 0.411 8.067 0.000 2.509 4.119
D&G 1.9512 0.411 4.752 0.000 1.146 2.756
DC Comics 2.1562 0.235 9.177 0.000 1.696 2.617
DC Direct -1.271e-15 8.64e-15 -0.147 0.883 -1.82e-14 1.57e-14
DC Shoes 1.7784 0.236 7.543 0.000 1.316 2.240
DCC 1.9449 0.645 3.017 0.003 0.681 3.208
DGK 1.9720 0.411 4.803 0.000 1.167 2.777
DIESEL 2.3276 0.264 8.808 0.000 1.810 2.846
DIRECTV 1.2078 0.655 1.844 0.065 -0.076 2.491
DJI 3.4595 0.648 5.336 0.000 2.189 4.730
DKNY 2.0095 0.262 7.665 0.000 1.496 2.523
DKNYC -6.631e-15 6.46e-15 -1.027 0.304 -1.93e-14 6.02e-15
DV by Dolce Vita 1.6038 0.645 2.488 0.013 0.341 2.867
DYMO 3.1219 0.484 6.450 0.000 2.173 4.071
Da-Nang 2.5912 0.646 4.012 0.000 1.325 3.857
Daisy Fuentes 1.5397 0.327 4.710 0.000 0.899 2.180
Dalia -1.341e-15 7.91e-15 -0.170 0.865 -1.68e-14 1.42e-14
Dana Buchman 1.6166 0.410 3.938 0.000 0.812 2.421
Daniel Cremieux 1.6192 0.413 3.919 0.000 0.809 2.429
Daniel Wellington 2.5967 0.645 4.026 0.000 1.333 3.861
Danielle Nicole 1.659e-14 8.53e-15 1.944 0.052 -1.36e-16 3.33e-14
Danier -1.208e-14 7.1e-15 -1.702 0.089 -2.6e-14 1.83e-15
Danny & Nicole 1.3988 0.520 2.689 0.007 0.379 2.418
Danskin 1.5952 0.246 6.487 0.000 1.113 2.077
Danskin Now 1.3764 0.327 4.213 0.000 0.736 2.017
Dansko 2.2112 0.380 5.813 0.000 1.466 2.957
David Taylor 1.302e-14 9.24e-15 1.409 0.159 -5.08e-15 3.11e-14
David Yurman 4.8082 0.261 18.397 0.000 4.296 5.320
David's Bridal 1.8471 0.256 7.204 0.000 1.345 2.350
Daytrip 1.9643 0.264 7.437 0.000 1.447 2.482
Dazzle Multimedia -1.39e-14 8.44e-15 -1.647 0.100 -3.05e-14 2.65e-15
DeLONG 2.2735 0.666 3.414 0.001 0.968 3.579
DeLonghi -1.676e-15 9.21e-15 -0.182 0.856 -1.97e-14 1.64e-14
DeMarini 2.162e-15 7.73e-15 0.280 0.780 -1.3e-14 1.73e-14
DeWALT® 2.2300 0.330 6.750 0.000 1.582 2.878
Deb 1.8027 0.644 2.797 0.005 0.540 3.066
Decree 1.3404 0.327 4.102 0.000 0.700 1.981
Deena & Ozzy 1.8712 0.645 2.903 0.004 0.608 3.135
Degree 2.3653 0.373 6.338 0.000 1.634 3.097
Deletta 1.6282 0.480 3.394 0.001 0.688 2.568
Dell 2.1773 0.253 8.621 0.000 1.682 2.672
Delta -9.919e-15 8.27e-15 -1.200 0.230 -2.61e-14 6.29e-15
Delta Burke 1.5591 0.644 2.419 0.016 0.296 2.822
Denim & Co. 1.3278 0.480 2.765 0.006 0.387 2.269
Dennis Basso 2.9674 0.647 4.585 0.000 1.699 4.236
Derek Heart 1.6020 0.345 4.642 0.000 0.926 2.278
Dermablend 2.1936 0.259 8.483 0.000 1.687 2.700
Design History 1.3683 0.646 2.118 0.034 0.102 2.635
Desigual 2.7278 0.645 4.232 0.000 1.464 3.991
Deux Lux 1.6890 0.645 2.620 0.009 0.426 2.952
Dex Baby 1.1311 0.648 1.744 0.081 -0.140 2.402
Diadora 1.9667 0.645 3.051 0.002 0.703 3.230
Dial 2.0774 0.412 5.037 0.000 1.269 2.886
Diamond Supply Co. 2.0643 0.246 8.390 0.000 1.582 2.547
Diane Gilman 1.15e-14 8.07e-15 1.425 0.154 -4.31e-15 2.73e-14
Diane von Furstenberg 2.1187 0.345 6.138 0.000 1.442 2.795
Dickies 1.8574 0.246 7.541 0.000 1.375 2.340
Dior 1.9332 0.644 3.000 0.003 0.670 3.196
Discovery Kids 2.1199 0.645 3.287 0.001 0.856 3.384
Disguise -1.87e-14 9.39e-15 -1.991 0.047 -3.71e-14 -2.89e-16
Disney 1.8909 0.213 8.868 0.000 1.473 2.309
Disney Baby 2.0909 0.481 4.346 0.000 1.148 3.034
Disney Collection 2.3492 0.244 9.646 0.000 1.872 2.827
Disney Jr. 2.1877 0.294 7.430 0.000 1.611 2.765
Disney Pixar 2.2884 0.268 8.535 0.000 1.763 2.814
Disney Pixar Cars 1.8033 0.264 6.831 0.000 1.286 2.321
Disney Princess 1.8319 0.246 7.437 0.000 1.349 2.315
Disney Tsum Tsum 1.9062 0.268 7.111 0.000 1.381 2.432
Dittos 2.08e-15 8.13e-15 0.256 0.798 -1.39e-14 1.8e-14
Divided 1.8808 0.371 5.070 0.000 1.154 2.608
Dockers 1.7689 0.298 5.929 0.000 1.184 2.354
Doctor Who 1.0838 0.645 1.681 0.093 -0.180 2.347
Dogeared 2.0169 0.644 3.130 0.002 0.754 3.280
Dogswell -1.094e-14 7.07e-15 -1.548 0.122 -2.48e-14 2.91e-15
Dolce & Gabbana 2.3072 0.313 7.364 0.000 1.693 2.921
Dolce Vita 2.2512 0.281 8.020 0.000 1.701 2.801
Dollhouse 1.6316 0.302 5.395 0.000 1.039 2.224
Donald J. Pliner 1.5523 0.645 2.408 0.016 0.289 2.816
Donald Trump 1.3491 0.645 2.093 0.036 0.086 2.612
Donna Morgan 3.0228 0.645 4.686 0.000 1.758 4.287
Dooney & Bourke 2.5845 0.219 11.803 0.000 2.155 3.014
Dots 1.5478 0.371 4.173 0.000 0.821 2.275
Double Zero 1.5378 0.645 2.386 0.017 0.274 2.801
Dove 2.0761 0.271 7.674 0.000 1.546 2.606
Dr. Brown's 2.3717 0.268 8.851 0.000 1.846 2.897
Dr. Hauschka 2.2738 0.644 3.528 0.000 1.011 3.537
Dr. Martens 2.4354 0.236 10.310 0.000 1.972 2.898
Dr. Scholl's 1.7476 0.294 5.951 0.000 1.172 2.323
Dr. Seuss -3.166e-15 9.34e-15 -0.339 0.735 -2.15e-14 1.51e-14
Dragon -1.445e-14 6.28e-15 -2.299 0.022 -2.68e-14 -2.13e-15
Dragon Ball Z 2.0551 0.412 4.985 0.000 1.247 2.863
Drake 2.0289 0.371 5.466 0.000 1.301 2.756
Dream Lites 1.7257 0.481 3.590 0.000 0.783 2.668
Dream On Me 2.2217 0.741 2.998 0.003 0.769 3.674
Dreamgirl 1.2655 0.647 1.955 0.051 -0.003 2.534
Dreamworks 1.7659 0.287 6.159 0.000 1.204 2.328
Dress Barn 1.5980 0.480 3.331 0.001 0.658 2.538
Drunk Elephant 2.071e-15 8.33e-15 0.249 0.804 -1.43e-14 1.84e-14
Duck Commander 1.5562 0.645 2.412 0.016 0.291 2.821
Duck Head 1.1037 0.647 1.706 0.088 -0.165 2.372
Duo Maternity 2.4220 0.650 3.727 0.000 1.148 3.696
Dynasty 1.6676 0.645 2.587 0.010 0.404 2.931
EA Sports 2.4526 0.645 3.804 0.000 1.189 3.716
ELF 0.8981 0.645 1.393 0.164 -0.366 2.162
ELLE 1.4724 0.313 4.702 0.000 0.859 2.086
ENERGIE 2.0053 0.481 4.172 0.000 1.063 2.947
EO 2.2492 0.297 7.583 0.000 1.668 2.831
Earl Jean 1.9426 0.411 4.725 0.000 1.137 2.748
Earl Jeans 3.086e-15 9.04e-15 0.341 0.733 -1.46e-14 2.08e-14
Earth Mama Angel Baby 1.8434 0.661 2.790 0.005 0.548 3.138
East 5th 1.1876 0.700 1.697 0.090 -0.184 2.560
Eastern Mountain Sports 1.4167 0.656 2.161 0.031 0.132 2.701
Eastessence 1.5940 0.645 2.471 0.013 0.330 2.858
Easton 2.9073 0.424 6.856 0.000 2.076 3.738
Ecko Red 1.6209 0.327 4.960 0.000 0.980 2.261
Ecko Unltd. 1.6366 0.480 3.411 0.001 0.696 2.577
Ed Hardy 1.5170 0.251 6.054 0.000 1.026 2.008
Eddie Bauer 1.9429 0.246 7.887 0.000 1.460 2.426
Educational Insights 2.5026 0.645 3.879 0.000 1.238 3.767
Eileen Fisher 1.6924 0.645 2.623 0.009 0.428 2.957
Elan -9.211e-15 7.63e-15 -1.207 0.227 -2.42e-14 5.74e-15
Elegant Moments 2.2622 0.644 3.511 0.000 0.999 3.525
Element 1.7670 0.410 4.305 0.000 0.963 2.571
Elements 1.9424 0.645 3.010 0.003 0.678 3.207
Elementz 1.2097 0.645 1.876 0.061 -0.054 2.473
Elie Tahari 3.1472 0.645 4.883 0.000 1.884 4.411
Elite 2.2905 0.645 3.553 0.000 1.027 3.554
Eliza J 2.1918 0.645 3.401 0.001 0.929 3.455
Elizabeth Arden 1.9502 0.293 6.646 0.000 1.375 2.525
Elizabeth and James 2.5788 0.481 5.360 0.000 1.636 3.522
Ellen Tracy 1.9301 0.412 4.683 0.000 1.122 2.738
Elmers 1.6285 0.222 7.322 0.000 1.193 2.064
Emerald Sundae 5.063e-15 8.41e-15 0.602 0.547 -1.14e-14 2.16e-14
Emerica 1.7133 0.480 3.569 0.000 0.772 2.654
Emerson 3.1854 0.655 4.864 0.000 1.902 4.469
Emilio Pucci 3.2164 0.480 6.703 0.000 2.276 4.157
Emma James -5.877e-16 9.92e-15 -0.059 0.953 -2e-14 1.88e-14
Empire 1.6588 0.372 4.462 0.000 0.930 2.387
Emporio Armani 2.4753 0.480 5.152 0.000 1.534 3.417
En Focus Studio 1.5509 0.480 3.231 0.001 0.610 2.492
Energizer® 1.4280 0.489 2.919 0.004 0.469 2.387
Enfagrow 1.5899 0.361 4.398 0.000 0.881 2.298
Enfamil 2.3331 0.253 9.221 0.000 1.837 2.829
Enyce 1.9013 0.645 2.946 0.003 0.637 3.166
Enzo Angiolini 2.2455 0.645 3.484 0.000 0.982 3.509
Epson 2.3429 0.356 6.580 0.000 1.645 3.041
Eric Carle 1.4952 0.484 3.092 0.002 0.548 2.443
Ermenegildo Zegna 2.0509 0.646 3.172 0.002 0.784 3.318
Ernie Ball 1.9337 0.665 2.908 0.004 0.630 3.237
Esprit 2.0164 0.652 3.092 0.002 0.738 3.295
Essence of Beauty -4.375e-15 7.5e-15 -0.583 0.560 -1.91e-14 1.03e-14
Essie -1.071e-14 8.71e-15 -1.229 0.219 -2.78e-14 6.36e-15
Estee Lauder 2.1887 0.221 9.909 0.000 1.756 2.622
Etienne Aigner 2.3691 0.653 3.629 0.000 1.089 3.649
Eucerin 2.5424 0.646 3.938 0.000 1.277 3.808
Euro-Pro 2.5327 0.673 3.764 0.000 1.214 3.852
Evan Picone 4.027e-16 1.01e-14 0.040 0.968 -1.94e-14 2.03e-14
Evenflo 1.9667 0.294 6.684 0.000 1.390 2.543
Everlane 1.293e-14 7.04e-15 1.836 0.066 -8.7e-16 2.67e-14
Everlast 1.6347 0.331 4.937 0.000 0.986 2.284
Everly Grey 2.5173 0.648 3.887 0.000 1.248 3.787
Evie 1.2969 0.656 1.978 0.048 0.012 2.582
EvoShield 6.378e-15 6.9e-15 0.925 0.355 -7.14e-15 1.99e-14
Exo-Terra 1.1645 0.672 1.733 0.083 -0.153 2.482
Express 1.8922 0.216 8.761 0.000 1.469 2.315
Eyeshadow 1.1620 0.480 2.420 0.016 0.221 2.103
FOREVER 21 1.6296 0.213 7.656 0.000 1.212 2.047
FULL TILT 1.6768 0.254 6.592 0.000 1.178 2.175
Faber-Castell 2.1375 0.645 3.314 0.001 0.873 3.402
Fabletics 1.8184 0.286 6.349 0.000 1.257 2.380
Fabulous Frannie 1.2197 0.646 1.889 0.059 -0.046 2.485
Faded Glory 1.3899 0.233 5.966 0.000 0.933 1.846
Faithfull the Brand 3.6400 0.644 5.648 0.000 2.377 4.903
Famous Stars & Straps 2.1565 0.480 4.494 0.000 1.216 3.097
Fang 1.8727 0.644 2.906 0.004 0.610 3.136
Fantasie -8.47e-16 7.33e-15 -0.116 0.908 -1.52e-14 1.35e-14
Farberware 2.0236 0.337 5.999 0.000 1.362 2.685
Fashion Bug 1.8470 0.327 5.651 0.000 1.206 2.488
Fashion Forms -1.099e-14 9.08e-15 -1.211 0.226 -2.88e-14 6.8e-15
Fashion Nova 2.0017 0.224 8.956 0.000 1.564 2.440
Fashion to Figure -8.313e-15 7.05e-15 -1.179 0.238 -2.21e-14 5.5e-15
Fatal Clothing 1.5987 0.645 2.480 0.013 0.335 2.862
Febreze 1.9075 0.276 6.907 0.000 1.366 2.449
Fellowes 1.902e-14 7.43e-15 2.558 0.011 4.45e-15 3.36e-14
Fender 2.6823 0.440 6.101 0.000 1.821 3.544
Fendi 2.9739 0.262 11.360 0.000 2.461 3.487
Ferasali 2.6731 0.480 5.572 0.000 1.733 3.613
Ferragamo 3.0816 0.482 6.393 0.000 2.137 4.026
Ferrari 2.2814 0.481 4.745 0.000 1.339 3.224
Field & Stream 1.7945 0.372 4.819 0.000 1.065 2.524
Fifth Sun -4.664e-18 9.32e-15 -0.001 1.000 -1.83e-14 1.83e-14
Fila 1.7835 0.261 6.831 0.000 1.272 2.295
Fila Sport 1.6118 0.327 4.931 0.000 0.971 2.252
Fiore 1.2345 0.480 2.572 0.010 0.294 2.175
Fire Los Angeles 1.8473 0.644 2.866 0.004 0.584 3.111
Fisher-Price 2.0208 0.224 9.034 0.000 1.582 2.459
Fisher-Price Baby 2.4508 0.413 5.937 0.000 1.642 3.260
Fitbit 2.6587 0.228 11.686 0.000 2.213 3.105
Fjallraven 2.3455 0.647 3.625 0.000 1.077 3.614
Flexees 1.4980 0.411 3.646 0.000 0.693 2.303
Flirtitude 1.6358 0.645 2.537 0.011 0.372 2.899
Floam 1.6574 0.246 6.727 0.000 1.174 2.140
Floppy Products 2.4541 0.680 3.609 0.000 1.122 3.787
Florsheim 1.2753 0.645 1.979 0.048 0.012 2.539
Fluval 2.4741 0.648 3.819 0.000 1.204 3.744
Flying Monkey 1.5051 0.480 3.135 0.002 0.564 2.446
Flying Tomato 1.6973 0.644 2.634 0.008 0.434 2.960
FootJoy 2.2868 0.480 4.760 0.000 1.345 3.228
For Joseph -2.091e-15 6.79e-15 -0.308 0.758 -1.54e-14 1.12e-14
For Love and Lemons 3.5436 0.410 8.635 0.000 2.739 4.348
Ford 2.6030 0.646 4.031 0.000 1.337 3.869
Forever Unique 1.3415 0.644 2.081 0.037 0.078 2.605
Fossil 2.2639 0.221 10.235 0.000 1.830 2.697
Fourstar Clothing 1.8863 0.645 2.926 0.003 0.623 3.150
Fox 2.1867 0.502 4.353 0.000 1.202 3.171
Fox Racing 1.9828 0.234 8.456 0.000 1.523 2.442
Frame Denim 3.3493 0.480 6.981 0.000 2.409 4.290
Francesca's Collections 1.6437 0.280 5.861 0.000 1.094 2.193
Franco Sarto 1.9369 0.345 5.610 0.000 1.260 2.614
Frankie B 1.5393 0.650 2.370 0.018 0.266 2.812
Frankie's Bikinis 3.3928 0.313 10.827 0.000 2.779 4.007
Franklin 1.4498 0.491 2.950 0.003 0.487 2.413
Franklin Learning Systems -6.963e-16 7.95e-15 -0.088 0.930 -1.63e-14 1.49e-14
Freddy 3.0858 0.645 4.787 0.000 1.822 4.349
Frederick's of Hollywood 2.0318 0.411 4.940 0.000 1.226 2.838
Free Country 1.9838 0.411 4.826 0.000 1.178 2.789
Free People 2.4407 0.216 11.309 0.000 2.018 2.864
Freestyle 2.2570 0.645 3.499 0.000 0.993 3.521
French Connection 2.3339 0.411 5.684 0.000 1.529 3.139
French Laundry 1.5643 0.371 4.215 0.000 0.837 2.292
French Toast 3.928e-15 7.41e-15 0.530 0.596 -1.06e-14 1.84e-14
Fresh 1.9248 0.645 2.985 0.003 0.661 3.188
Freya -6.634e-15 8.46e-15 -0.784 0.433 -2.32e-14 9.96e-15
Frontline 2.4878 0.480 5.178 0.000 1.546 3.430
Fruit of the Loom 1.6384 0.327 5.015 0.000 0.998 2.279
Frye 3.0596 0.254 12.024 0.000 2.561 3.558
Fuji 2.0144 0.239 8.428 0.000 1.546 2.483
Fujifilm 2.8401 0.649 4.373 0.000 1.567 4.113
Fun World 1.9329 0.412 4.695 0.000 1.126 2.740
Funimation Production 1.9549 0.372 5.258 0.000 1.226 2.684
Funko 2.2745 0.215 10.575 0.000 1.853 2.696
Furla 3.1990 0.411 7.792 0.000 2.394 4.004
Furminator 3.0587 0.646 4.733 0.000 1.792 4.325
G by Guess 1.7991 0.294 6.129 0.000 1.224 2.374
G-Shock 2.3154 0.314 7.369 0.000 1.700 2.931
G-Star Raw 2.3458 0.411 5.705 0.000 1.540 3.152
G.I. JOE 1.7812 0.645 2.762 0.006 0.517 3.045
GAIAM 2.1677 0.652 3.325 0.001 0.890 3.446
GAIN 1.8105 0.414 4.371 0.000 0.999 2.622
GAME 4.431e-16 7.41e-15 0.060 0.952 -1.41e-14 1.5e-14
GE 2.1433 0.654 3.278 0.001 0.862 3.425
GEAR FOR SPORTS 0.9692 0.646 1.501 0.133 -0.296 2.235
GM Performance Parts -1.707e-16 5.94e-15 -0.029 0.977 -1.18e-14 1.15e-14
GUESS 1.9066 0.218 8.731 0.000 1.479 2.335
Galaxy 9.801e-15 8.91e-15 1.100 0.271 -7.66e-15 2.73e-14
Gallery 1.9819 0.649 3.053 0.002 0.710 3.254
Ganz 2.0164 0.263 7.666 0.000 1.501 2.532
Gap 1.8536 0.215 8.641 0.000 1.433 2.274
Garage 1.8532 0.644 2.876 0.004 0.590 3.116
Garmin 2.4863 0.276 9.023 0.000 1.946 3.026
Garnet Hill 1.7891 0.645 2.775 0.006 0.525 3.053
Garnier 1.8672 0.255 7.328 0.000 1.368 2.367
Gateway 2.0897 0.647 3.229 0.001 0.821 3.358
General Electric 1.4900 0.372 4.003 0.000 0.760 2.220
Generic 1.0169 0.644 1.578 0.115 -0.246 2.280
Geneva 1.0502 0.314 3.341 0.001 0.434 1.666
Gentle Souls 2.6510 0.645 4.113 0.000 1.388 3.914
Geoffrey Beene 2.2256 0.483 4.610 0.000 1.279 3.172
George 1.4932 0.373 4.007 0.000 0.763 2.224
George Foreman 2.1537 0.485 4.444 0.000 1.204 3.104
George Simonton 1.7231 0.645 2.672 0.008 0.459 2.987
Georgio Armani 2.5569 0.243 10.518 0.000 2.080 3.033
Gerber 1.7500 0.259 6.756 0.000 1.242 2.258
Gerber Good Start 3.5126 0.382 9.187 0.000 2.763 4.262
Giacca 1.038e-14 7.54e-15 1.378 0.168 -4.39e-15 2.52e-14
Giani Bernini 1.0310 0.645 1.600 0.110 -0.232 2.294
Gianni Bini 1.7768 0.411 4.327 0.000 0.972 2.582
Gildan 1.9372 0.225 8.592 0.000 1.495 2.379
Gillette 2.0974 0.248 8.463 0.000 1.612 2.583
Gilligan & O'Malley 1.7033 0.276 6.180 0.000 1.163 2.244
Gilly Hicks 1.6729 0.302 5.533 0.000 1.080 2.265
Giorgio Armani 2.1993 0.481 4.576 0.000 1.257 3.141
Gitano 1.9955 0.645 3.095 0.002 0.732 3.259
Giuseppe Zanotti 3.8379 0.645 5.951 0.000 2.574 5.102
Givenchy 2.5678 0.264 9.729 0.000 2.050 3.085
Glade 1.7663 0.293 6.023 0.000 1.192 2.341
Glo Jeans 5.642e-15 8.2e-15 0.688 0.491 -1.04e-14 2.17e-14
Globe -1.2e-14 1.01e-14 -1.185 0.236 -3.18e-14 7.84e-15
Gloria Vanderbilt 2.1659 0.346 6.258 0.000 1.488 2.844
Go Gear 2.9177 0.352 8.294 0.000 2.228 3.607
GoPro 4.1980 0.649 6.464 0.000 2.925 5.471
Goddess 2.1877 0.644 3.395 0.001 0.925 3.451
Godinger -1.489e-14 8.95e-15 -1.664 0.096 -3.24e-14 2.65e-15
Gold Bug 2.0601 0.680 3.030 0.002 0.728 3.393
Gold's Gym 1.3980 0.645 2.167 0.030 0.133 2.663
Good Cook 0.5919 0.654 0.905 0.365 -0.690 1.874
Good Technology 1.7588 0.328 5.362 0.000 1.116 2.402
Goodyear 2.0604 0.652 3.159 0.002 0.782 3.339
Gottex 3.202e-15 8.76e-15 0.365 0.715 -1.4e-14 2.04e-14
Goyard 3.3184 0.646 5.136 0.000 2.052 4.585
Grace Elements 1.7778 0.645 2.757 0.006 0.514 3.042
Grace In LA 2.1830 0.346 6.301 0.000 1.504 2.862
Grace Karin 2.1720 0.645 3.367 0.001 0.908 3.436
Graco 2.5296 0.323 7.832 0.000 1.897 3.163
Great Expectations 2.2902 0.650 3.524 0.000 1.016 3.564
Greenlight Collectibles -1.264e-14 8.63e-15 -1.464 0.143 -2.96e-14 4.28e-15
Griffin 1.0684 0.481 2.223 0.026 0.126 2.011
Gucci 3.4234 0.219 15.609 0.000 2.994 3.853
Guerlain 2.5567 0.345 7.413 0.000 1.881 3.233
Guess by Marciano 2.4080 0.645 3.736 0.000 1.145 3.671
Gund 1.8234 0.411 4.433 0.000 1.017 2.630
Guy Harvey 1.9338 0.328 5.899 0.000 1.291 2.576
Gymboree 1.9175 0.218 8.789 0.000 1.490 2.345
Gymshark 2.6941 0.222 12.162 0.000 2.260 3.128
H&M 1.6645 0.216 7.717 0.000 1.242 2.087
HDE 0.6538 0.645 1.013 0.311 -0.611 1.919
HEAD 1.8438 0.424 4.344 0.000 1.012 2.676
HERMES 2.9223 0.294 9.934 0.000 2.346 3.499
HOBO INTERNATIONAL 2.9313 0.480 6.106 0.000 1.990 3.872
HP 2.3692 0.235 10.082 0.000 1.909 2.830
HTC 1.9022 0.281 6.768 0.000 1.351 2.453
HUF 1.8911 0.411 4.605 0.000 1.086 2.696
HYDRAULIC 1.5816 0.314 5.039 0.000 0.966 2.197
Haggar 2.2231 0.653 3.402 0.001 0.942 3.504
Hailey Logan 2.9117 0.645 4.514 0.000 1.647 4.176
Hallmark 1.6798 0.246 6.832 0.000 1.198 2.162
Halo 1.5742 0.350 4.498 0.000 0.888 2.260
Halogen 1.5601 0.411 3.799 0.000 0.755 2.365
Halston Heritage 3.0812 0.645 4.781 0.000 1.818 4.345
Hamilton Beach 1.5757 0.491 3.211 0.001 0.614 2.537
Hanes 1.5216 0.281 5.419 0.000 0.971 2.072
Hang Ten 1.4579 0.481 3.031 0.002 0.515 2.400
Hanna Anderson 1.9930 0.245 8.145 0.000 1.513 2.473
Hannspree 1.3152 0.677 1.943 0.052 -0.012 2.642
Happy Socks 1.4964 0.645 2.319 0.020 0.232 2.761
Harajuku Lovers 1.5745 0.345 4.560 0.000 0.898 2.251
Hard Candy 1.4830 0.271 5.471 0.000 0.952 2.014
Hard Tail 1.6600 0.645 2.576 0.010 0.397 2.923
Harley-Davidson 2.1991 0.219 10.047 0.000 1.770 2.628
Harman Kardon 1.7947 0.481 3.734 0.000 0.853 2.737
Hasbro 2.2527 0.224 10.079 0.000 1.815 2.691
Hasbro Games 1.9061 0.412 4.623 0.000 1.098 2.714
Havaianas 1.7556 0.480 3.657 0.000 0.815 2.696
Hawk 1.0500 0.645 1.629 0.103 -0.213 2.313
Heart Soul 1.7867 0.302 5.911 0.000 1.194 2.379
Hearthside Collection 3.801e-15 9.29e-15 0.409 0.683 -1.44e-14 2.2e-14
Helix 1.8855 0.648 2.910 0.004 0.615 3.156
Hello Kitty 1.7717 0.222 7.966 0.000 1.336 2.208
Helly Hansen 1.9270 0.348 5.530 0.000 1.244 2.610
Helmut Lang 2.2522 0.489 4.608 0.000 1.294 3.210
Henri Bendel 2.7445 0.286 9.582 0.000 2.183 3.306
Heritage 1.5052 0.481 3.128 0.002 0.562 2.449
Hermès 3.7361 0.656 5.694 0.000 2.450 5.022
Herschel Supply Company 2.3630 0.348 6.785 0.000 1.680 3.046
Hi-Tec 6.792e-17 9.52e-15 0.007 0.994 -1.86e-14 1.87e-14
High Sierra 1.782e-15 6.49e-15 0.275 0.784 -1.09e-14 1.45e-14
Hillard & Hanson -1.13e-14 8.54e-15 -1.323 0.186 -2.8e-14 5.44e-15
Hind 2.0955 0.644 3.252 0.001 0.832 3.359
Hippie -9.381e-15 9.47e-15 -0.991 0.322 -2.79e-14 9.18e-15
Hitachi 2.4952 0.487 5.127 0.000 1.541 3.449
HoMedics 1.8602 0.412 4.520 0.000 1.054 2.667
Hobie 7.828e-15 7.32e-15 1.070 0.285 -6.51e-15 2.22e-14
Hoka One 6.385e-15 7.32e-15 0.872 0.383 -7.96e-15 2.07e-14
Hollister 1.7584 0.214 8.222 0.000 1.339 2.178
Hollister Co. 1.7108 0.238 7.187 0.000 1.244 2.177
Home Interiors 2.2086 0.272 8.115 0.000 1.675 2.742
Honey Punch 2.7351 0.645 4.240 0.000 1.471 4.000
Hot Kiss 1.4141 0.313 4.516 0.000 0.800 2.028
Hot Sox 6.07e-16 8.93e-15 0.068 0.946 -1.69e-14 1.81e-14
Hot Topic 1.7514 0.216 8.101 0.000 1.328 2.175
Hot Wheels 1.4191 0.249 5.688 0.000 0.930 1.908
Hourglass 2.2656 0.645 3.513 0.000 1.002 3.529
Hourglass Cosmetics 2.6761 0.275 9.714 0.000 2.136 3.216
House of Harlow 1960 2.5325 0.645 3.929 0.000 1.269 3.796
Hoverboard 3.2198 0.424 7.599 0.000 2.389 4.050
Hublot 1.501e-14 8.12e-15 1.849 0.065 -9.05e-16 3.09e-14
Huda Beauty 2.2670 0.241 9.392 0.000 1.794 2.740
Hudson Jeans 2.5077 0.257 9.774 0.000 2.005 3.011
Hue 1.0621 0.645 1.647 0.100 -0.202 2.326
Huggies 1.9567 0.306 6.398 0.000 1.357 2.556
Huggies Little Movers 2.3738 0.494 4.804 0.000 1.405 3.342
Huggies Little Snugglers 1.9752 0.365 5.414 0.000 1.260 2.690
Huggies Pull-Ups 2.0041 0.427 4.692 0.000 1.167 2.841
Huggies Snug & Dry 2.0973 0.335 6.268 0.000 1.441 2.753
Hugo Boss 2.4551 0.656 3.742 0.000 1.169 3.741
Hula Honey 1.8807 0.481 3.909 0.000 0.938 2.824
Hunter 2.8577 0.229 12.461 0.000 2.408 3.307
Hunter Boots 2.8115 0.303 9.292 0.000 2.218 3.405
Hurley 1.7046 0.253 6.730 0.000 1.208 2.201
Hybrid 1.2639 0.646 1.957 0.050 -0.002 2.530
Hype -6.752e-15 7.86e-15 -0.859 0.390 -2.22e-14 8.66e-15
IBM 2.4929 0.647 3.853 0.000 1.225 3.761
IKEA 1.6701 0.254 6.564 0.000 1.171 2.169
INC International Concepts 1.7552 0.245 7.173 0.000 1.276 2.235
ION Audio 6.498e-15 9.67e-15 0.672 0.502 -1.25e-14 2.55e-14
IT Cosmetics 2.2598 0.225 10.028 0.000 1.818 2.701
IZOD 1.9817 0.303 6.545 0.000 1.388 2.575
Icon 2.5244 0.648 3.895 0.000 1.254 3.795
Ideology 1.8326 0.644 2.844 0.004 0.570 3.096
Illamasqua 2.4207 0.475 5.093 0.000 1.489 3.352
Imagine Baby 1.5598 0.652 2.394 0.017 0.283 2.837
Imaginext 2.3604 0.295 8.014 0.000 1.783 2.938
Incipio 1.9675 0.287 6.862 0.000 1.405 2.529
Independent 2.3689 0.214 11.063 0.000 1.949 2.789
Indigo Moon 1.4678 0.649 2.262 0.024 0.196 2.740
Industrial Cotton 1.4254 0.646 2.205 0.027 0.158 2.692
Infantino 1.9262 0.297 6.478 0.000 1.343 2.509
Infinity 1.9527 0.480 4.068 0.000 1.012 2.893
Infinity Raine 2.1682 0.645 3.362 0.001 0.904 3.432
Ingenuity 2.8490 0.514 5.541 0.000 1.841 3.857
Inglot 1.6511 0.410 4.023 0.000 0.847 2.455
Ingrid & Isabel 0.3507 0.739 0.475 0.635 -1.097 1.798
Insignia 1.9737 0.666 2.962 0.003 0.668 3.280
Intel 2.6886 0.414 6.492 0.000 1.877 3.500
InterDesign 1.5052 0.482 3.125 0.002 0.561 2.449
Invicta 2.2629 0.411 5.500 0.000 1.457 3.069
Invicta Watch Group 1.49e-14 8.52e-15 1.748 0.080 -1.8e-15 3.16e-14
Iris 1.3461 0.480 2.805 0.005 0.406 2.287
Irish Spring 2.6376 0.646 4.085 0.000 1.372 3.903
Iron Fist 2.3330 0.302 7.715 0.000 1.740 2.926
Iron Maiden 2.2400 0.645 3.475 0.001 0.977 3.503
Isaac Mizrahi 2.6910 0.645 4.175 0.000 1.428 3.954
Isabel Marant 3.011e-15 7.49e-15 0.402 0.688 -1.17e-14 1.77e-14
Island Escape 2.1914 0.645 3.400 0.001 0.928 3.455
It's Our Time 1.087e-14 8.73e-15 1.246 0.213 -6.23e-15 2.8e-14
Itzy Ritzy -1.913e-14 8.46e-15 -2.262 0.024 -3.57e-14 -2.55e-15
Ivanka Trump 2.5846 0.480 5.386 0.000 1.644 3.525
Ivory Ella 2.2997 0.264 8.711 0.000 1.782 2.817
Ivy & Blu 2.2328 0.645 3.464 0.001 0.970 3.496
Iz Byer 1.7279 0.480 3.602 0.000 0.788 2.668
J Brand 2.5167 0.481 5.229 0.000 1.573 3.460
J&L 1.0092 0.645 1.566 0.117 -0.254 2.273
J. Crew 2.0921 0.220 9.510 0.000 1.661 2.523
J. Jill 1.9824 0.481 4.124 0.000 1.040 2.925
J.Crew 1.9481 0.480 4.061 0.000 1.008 2.888
J.Lo by Jennifer Lopez 1.7085 0.480 3.560 0.000 0.768 2.649
JBL 2.7214 0.349 7.789 0.000 2.037 3.406
JC Toys 2.0623 0.645 3.198 0.001 0.798 3.326
JCPenney 1.7962 0.247 7.263 0.000 1.311 2.281
JERZEES 1.9960 0.376 5.311 0.000 1.259 2.733
JJ Cole Collections 1.8386 0.482 3.816 0.000 0.894 2.783
JM Collection -7.579e-15 9.09e-15 -0.834 0.404 -2.54e-14 1.02e-14
JS Boutique 2.1560 0.645 3.342 0.001 0.892 3.420
JVC 6.505e-15 8.05e-15 0.808 0.419 -9.27e-15 2.23e-14
Jac Vanek -7.524e-15 9.31e-15 -0.809 0.419 -2.58e-14 1.07e-14
Jack Rogers 2.3806 0.327 7.282 0.000 1.740 3.021
Jaclyn Smith 1.3077 0.411 3.182 0.001 0.502 2.113
Jada Toys 1.7474 0.480 3.637 0.000 0.806 2.689
Jade 3.4608 0.644 5.370 0.000 2.198 4.724
Jafra 2.2040 0.410 5.370 0.000 1.400 3.008
Jagermeister -2.551e-15 6.68e-15 -0.382 0.702 -1.56e-14 1.05e-14
Jakks Pacific 1.6459 0.346 4.756 0.000 0.968 2.324
Jamberry 2.0497 0.483 4.248 0.000 1.104 2.995
Jambu 2.2851 0.645 3.545 0.000 1.022 3.548
James Avery 3.0832 0.231 13.327 0.000 2.630 3.537
James Jeans 4.178e-15 9.76e-15 0.428 0.669 -1.5e-14 2.33e-14
James Perse 1.9585 0.410 4.772 0.000 1.154 2.763
JanSport 2.0068 0.330 6.085 0.000 1.360 2.653
Jane Iredale 2.0460 0.480 4.259 0.000 1.104 2.988
Jason Wu 2.2445 0.649 3.459 0.001 0.972 3.516
Jay at Play 2.0967 0.656 3.195 0.001 0.811 3.383
Jazwares 1.436e-15 6.81e-15 0.211 0.833 -1.19e-14 1.48e-14
Jean Paul Gaultier -8.106e-15 8.88e-15 -0.913 0.361 -2.55e-14 9.3e-15
Jeanne Pierre 1.5595 0.645 2.419 0.016 0.296 2.823
Jeep 2.2203 0.700 3.172 0.002 0.848 3.592
Jeffery Campbell 2.8070 0.264 10.624 0.000 2.289 3.325
Jeffree Star Cosmetics 2.4358 0.226 10.772 0.000 1.993 2.879
Jeffrey Campbell 2.7543 0.480 5.739 0.000 1.814 3.695
Jelly Belly 9.103e-15 1.02e-14 0.894 0.371 -1.09e-14 2.91e-14
Jennifer Lopez 1.8300 0.281 6.523 0.000 1.280 2.380
Jensen 2.6039 0.507 5.137 0.000 1.610 3.597
Jessica Howard 2.0395 0.482 4.232 0.000 1.095 2.984
Jessica McClintock 2.3021 0.480 4.797 0.000 1.361 3.243
Jessica Simpson 1.9051 0.229 8.306 0.000 1.456 2.355
Jim Shore 2.2756 0.327 6.957 0.000 1.634 2.917
Jimmy Choo 3.4561 0.313 11.038 0.000 2.842 4.070
Jockey 2.1589 0.371 5.813 0.000 1.431 2.887
Jodi Kristopher 2.4045 0.480 5.010 0.000 1.464 3.345
Joe Benbasset 1.5774 0.374 4.216 0.000 0.844 2.311
Joe's Jeans 1.9526 0.272 7.191 0.000 1.420 2.485
John Deere 1.4519 0.328 4.430 0.000 0.810 2.094
Johnson & Johnson 2.0516 0.336 6.102 0.000 1.393 2.711
Johnston & Murphy -1.295e-15 8.4e-15 -0.154 0.878 -1.78e-14 1.52e-14
Joie 2.6604 0.480 5.545 0.000 1.720 3.601
Jolt 1.6689 0.345 4.833 0.000 0.992 2.346
Jolyn Clothing 2.4716 0.411 6.016 0.000 1.666 3.277
Jones New York 1.6466 0.313 5.259 0.000 1.033 2.260
Jonesport 2.0064 0.647 3.103 0.002 0.739 3.274
Joovy 3.2791 0.775 4.233 0.000 1.761 4.798
Jordache 1.2929 0.647 1.997 0.046 0.024 2.562
Jordan 2.4739 0.220 11.269 0.000 2.044 2.904
Jordan Craig 1.9731 0.691 2.857 0.004 0.620 3.327
Jordans 2.5486 0.228 11.173 0.000 2.102 2.996
Joseph Abboud 2.2816 0.655 3.481 0.000 0.997 3.566
Josephine Chaus -2.505e-15 7.89e-15 -0.318 0.751 -1.8e-14 1.3e-14
Josie 1.6107 0.645 2.498 0.012 0.347 2.874
Josie Maran 2.3916 0.644 3.711 0.000 1.128 3.655
Josie Natori 1.7732 0.480 3.695 0.000 0.833 2.714
Joujou 1.9552 0.480 4.072 0.000 1.014 2.896
Joules -2.318e-15 9.1e-15 -0.255 0.799 -2.02e-14 1.55e-14
Journey Girls 2.3061 0.411 5.606 0.000 1.500 3.112
Jovani 3.8290 0.314 12.192 0.000 3.213 4.445
Judith March 4.475e-15 8.88e-15 0.504 0.614 -1.29e-14 2.19e-14
Juicy Couture 2.0379 0.219 9.312 0.000 1.609 2.467
Jump Apparel 2.0944 0.644 3.250 0.001 0.831 3.358
Jumping Beans 1.5996 0.276 5.805 0.000 1.060 2.140
Junk Food -1.441e-14 1.04e-14 -1.390 0.164 -3.47e-14 5.9e-15
Just Love 2.947e-15 7.75e-15 0.380 0.704 -1.22e-14 1.81e-14
Just My Size 1.5971 0.411 3.888 0.000 0.792 2.402
Just Play 2.9550 0.645 4.582 0.000 1.691 4.219
JustFab 1.8058 0.268 6.751 0.000 1.281 2.330
Justice 1.9634 0.221 8.898 0.000 1.531 2.396
Justin 1.7679 0.271 6.513 0.000 1.236 2.300
Justin Boots 2.3895 0.645 3.708 0.000 1.126 3.653
K'NEX 1.6450 0.651 2.526 0.012 0.369 2.921
K-SWISS 1.3217 0.371 3.562 0.000 0.594 2.049
KIKO MILANO 1.5726 0.645 2.440 0.015 0.309 2.836
KONG 1.7070 0.412 4.145 0.000 0.900 2.514
KUT from the Kloth 2.3989 0.481 4.989 0.000 1.457 3.341
KY 7.772e-15 6.46e-15 1.203 0.229 -4.89e-15 2.04e-14
Kae Argatherapie 0.7913 0.645 1.228 0.220 -0.472 2.055
Kala 2.6077 0.546 4.777 0.000 1.538 3.677
Kanga Care 1.5738 0.652 2.415 0.016 0.297 2.851
Kangol 7.032e-15 8.53e-15 0.825 0.410 -9.68e-15 2.37e-14
Kappa 2.1728 0.645 3.371 0.001 0.909 3.436
Kardashian Kollection 1.7365 0.286 6.062 0.000 1.175 2.298
Karen Kane 2.813e-15 8.92e-15 0.315 0.753 -1.47e-14 2.03e-14
Karen Scott 1.5609 0.371 4.205 0.000 0.833 2.288
Kasper 1.9538 0.647 3.018 0.003 0.685 3.223
Kat Von D 2.3167 0.218 10.623 0.000 1.889 2.744
Kat Von D Beauty 1.57e-15 8.56e-15 0.183 0.854 -1.52e-14 1.83e-14
Kate Landry 1.7355 0.645 2.693 0.007 0.472 2.999
Kate Somerville 2.3539 0.644 3.652 0.000 1.091 3.617
Kate Spade 2.8709 0.214 13.402 0.000 2.451 3.291
Kathy Van Zeeland 1.5530 0.411 3.780 0.000 0.748 2.358
Kay Jewelers 2.8142 0.327 8.614 0.000 2.174 3.455
Kaytee 1.7299 0.490 3.528 0.000 0.769 2.691
Keds 1.6562 0.327 5.066 0.000 1.015 2.297
Kelly & Katie 1.9706 0.645 3.057 0.002 0.707 3.234
Kelsi Dagger 4.105e-15 8.28e-15 0.496 0.620 -1.21e-14 2.03e-14
Kenar 1.3989 0.646 2.164 0.030 0.132 2.666
Kendall & Kylie 1.9401 0.294 6.610 0.000 1.365 2.515
Kendra Scott 3.6263 0.215 16.881 0.000 3.205 4.047
Kenneth Cole -4.779e-15 6.6e-15 -0.724 0.469 -1.77e-14 8.17e-15
Kenneth Cole New York 1.9258 0.267 7.200 0.000 1.402 2.450
Kenneth Cole Reaction 1.7929 0.348 5.145 0.000 1.110 2.476
Kenneth Jay Lane 6.097e-16 8.99e-15 0.068 0.946 -1.7e-14 1.82e-14
Kensie 1.8778 0.647 2.900 0.004 0.609 3.147
Kenwood 1.8877 0.533 3.542 0.000 0.843 2.932
Kenzo 2.2289 0.645 3.458 0.001 0.966 3.492
Keranique 2.1277 0.647 3.286 0.001 0.859 3.397
Keurig 2.2992 0.235 9.796 0.000 1.839 2.759
Kibbles 'n Bits 2.2931 0.412 5.561 0.000 1.485 3.101
KidCo -1.447e-14 7.27e-15 -1.992 0.046 -2.87e-14 -2.32e-16
Kids Preferred 1.37e-14 1.11e-14 1.240 0.215 -7.96e-15 3.54e-14
Kidsline 1.1451 0.648 1.766 0.077 -0.126 2.416
Kiehl's 2.2834 0.261 8.735 0.000 1.771 2.796
Kiinde 2.8477 0.651 4.375 0.000 1.572 4.124
Kim Rogers 1.2712 0.413 3.080 0.002 0.462 2.080
Kimchi Blue 1.9169 0.302 6.344 0.000 1.325 2.509
KinderGlo 1.7427 0.655 2.662 0.008 0.459 3.026
Kipling 1.3991 0.480 2.916 0.004 0.459 2.340
Kiplling 1.8312 0.303 6.045 0.000 1.237 2.425
Kirra 1.4339 0.480 2.989 0.003 0.494 2.374
KitchenAid 2.5217 0.284 8.866 0.000 1.964 3.079
Kleenex 1.9530 0.481 4.056 0.000 1.009 2.897
Klipsch -1.727e-14 8.94e-15 -1.933 0.053 -3.48e-14 2.45e-16
Koala Baby 1.4964 0.645 2.321 0.020 0.233 2.760
Kodak 1.4337 0.334 4.294 0.000 0.779 2.088
Kolcraft 0.3618 0.709 0.510 0.610 -1.029 1.752
Koman 1.4848 0.645 2.301 0.021 0.220 2.750
Konami 1.8240 0.230 7.947 0.000 1.374 2.274
Kooba 1.9285 0.645 2.991 0.003 0.665 3.192
Koret 2.1699 0.645 3.366 0.001 0.906 3.434
Kork-Ease 2.2679 0.645 3.518 0.000 1.005 3.531
Kotex 2.0325 0.279 7.297 0.000 1.487 2.578
Kuhl 3.1302 0.654 4.785 0.000 1.848 4.412
Kurgo 6.379e-15 8.85e-15 0.720 0.471 -1.1e-14 2.37e-14
Kylie Cosmetics 2.3069 0.216 10.663 0.000 1.883 2.731
Kyocera 0.3688 0.645 0.572 0.567 -0.895 1.633
Kyodan 1.9139 0.371 5.158 0.000 1.187 2.641
L'Oreal 1.6621 0.221 7.506 0.000 1.228 2.096
L*SPACE 2.2776 0.480 4.744 0.000 1.337 3.219
L.A. Blues 1.3396 0.647 2.070 0.038 0.071 2.608
L.A.M.B. 2.4689 0.327 7.551 0.000 1.828 3.110
L.L. Bean 2.6190 0.246 10.643 0.000 2.137 3.101
LA Hearts 1.6639 0.287 5.805 0.000 1.102 2.226
LA Idol 2.1993 0.346 6.361 0.000 1.522 2.877
LC Lauren Conrad 1.9150 0.234 8.198 0.000 1.457 2.373
LEGO 2.3561 0.236 9.983 0.000 1.894 2.819
LF 2.7754 0.247 11.228 0.000 2.291 3.260
LG 2.2105 0.228 9.691 0.000 1.763 2.658
LOFT 1.4013 0.371 3.777 0.000 0.674 2.129
LRG 2.3878 0.411 5.811 0.000 1.582 3.193
La Blanca 1.7859 0.645 2.771 0.006 0.523 3.049
La Femme 4.0327 0.645 6.251 0.000 2.768 5.297
La Perla 2.4754 0.644 3.841 0.000 1.212 3.739
LaCie 3.0428 0.654 4.651 0.000 1.761 4.325
Lacoste 2.1509 0.233 9.214 0.000 1.693 2.608
Lafayette New York 2.0908 0.645 3.243 0.001 0.827 3.354
Lalaloopsy 2.0988 0.277 7.578 0.000 1.556 2.642
Lamaze 5.406e-15 9.39e-15 0.576 0.565 -1.3e-14 2.38e-14
Lambs & Ivy 1.5102 0.648 2.329 0.020 0.239 2.781
Lancome 2.1829 0.220 9.913 0.000 1.751 2.615
Lancôme 1.8101 0.645 2.805 0.005 0.545 3.075
Lands' End 1.6683 0.294 5.677 0.000 1.092 2.244
Lane Bryant 1.7802 0.234 7.624 0.000 1.323 2.238
Lansinoh 1.9877 0.320 6.213 0.000 1.361 2.615
Lanvin 3.4155 0.645 5.296 0.000 2.152 4.679
Lapis 1.677e-14 7.29e-15 2.299 0.021 2.48e-15 3.11e-14
Laredo 2.6268 0.644 4.076 0.000 1.364 3.890
Larry Levine 1.6990 0.652 2.606 0.009 0.421 2.977
Last Kiss 1.3824 0.646 2.141 0.032 0.117 2.648
Laundry by Shelli Segal -2.371e-15 7.41e-15 -0.320 0.749 -1.69e-14 1.22e-14
Laura Ashley 2.0756 0.482 4.307 0.000 1.131 3.020
Laura Geller 2.1535 0.247 8.708 0.000 1.669 2.638
Laura Mercier 2.2655 0.233 9.704 0.000 1.808 2.723
Laura Scott 1.5281 0.411 3.719 0.000 0.723 2.333
Laurel Burch 2.6643 0.645 4.133 0.000 1.401 3.928
Lauren Conrad 1.9803 0.411 4.822 0.000 1.175 2.785
Lauren Ralph Lauren 2.1424 0.242 8.837 0.000 1.667 2.618
Le Creuset 2.1277 0.317 6.714 0.000 1.507 2.749
LeSportsac 2.2511 0.645 3.491 0.000 0.987 3.515
Leap Frog 2.1297 0.264 8.071 0.000 1.613 2.647
Lee 1.7360 0.295 5.894 0.000 1.159 2.313
Leith 1.6427 0.737 2.230 0.026 0.199 3.086
Lela Rose 1.1988 0.645 1.859 0.063 -0.065 2.463
Lenox 2.2578 0.295 7.658 0.000 1.680 2.836
Letarte 2.0269 0.644 3.145 0.002 0.764 3.290
Level 1.6763 0.645 2.599 0.009 0.412 2.940
Levi Strauss & Co. 2.2394 0.314 7.130 0.000 1.624 2.855
Levi's® 1.9284 0.216 8.915 0.000 1.504 2.352
Lexar 1.2796 0.654 1.956 0.050 -0.003 2.562
Lexmark 3.3629 0.655 5.130 0.000 2.078 4.648
Lia Sophia 1.7697 0.410 4.312 0.000 0.965 2.574
Liberty Apparel 1.8093 0.656 2.760 0.006 0.524 3.094
Life Is Good 1.6616 0.302 5.498 0.000 1.069 2.254
Life Stride 3.928e-15 8.67e-15 0.453 0.651 -1.31e-14 2.09e-14
Liliana -2.888e-15 8.13e-15 -0.355 0.722 -1.88e-14 1.3e-14
Lilly Pulitzer 2.6645 0.217 12.306 0.000 2.240 3.089
Lilly Pulitzer for Target 2.9874 0.480 6.226 0.000 2.047 3.928
Lily White 1.4956 0.412 3.634 0.000 0.689 2.302
Lilyette 1.4285 0.644 2.217 0.027 0.165 2.692
Lime Crime 2.1960 0.239 9.192 0.000 1.728 2.664
Lime Crime Cosmetics 3.4133 0.645 5.294 0.000 2.150 4.677
Limited Too 5.03e-15 8.15e-15 0.617 0.537 -1.09e-14 2.1e-14
Lincoln Logs 1.6462 0.651 2.528 0.011 0.370 2.922
Linea Donatella 9.521e-15 8.37e-15 1.138 0.255 -6.88e-15 2.59e-14
Linksys 1.4029 0.515 2.724 0.006 0.393 2.412
Lipsy 4.94e-15 7.82e-15 0.632 0.528 -1.04e-14 2.03e-14
Liquid Blue 2.1748 0.480 4.530 0.000 1.234 3.116
Listerine 4.628e-15 7.22e-15 0.641 0.521 -9.52e-15 1.88e-14
Lite Brix -3.602e-15 5.08e-15 -0.710 0.478 -1.35e-14 6.35e-15
Litter Genie 2.1675 0.648 3.346 0.001 0.898 3.437
Little Gifts -2.812e-15 8.79e-15 -0.320 0.749 -2e-14 1.44e-14
Little Tikes 2.1286 0.330 6.446 0.000 1.481 2.776
Littlest Pet Shop 1.7783 0.221 8.058 0.000 1.346 2.211
Live a Little 2.0301 0.645 3.149 0.002 0.766 3.294
Liz & Co. -5.111e-15 6.64e-15 -0.769 0.442 -1.81e-14 7.91e-15
Liz Claiborne 1.7102 0.256 6.673 0.000 1.208 2.212
Liz Lange 1.7177 0.246 6.969 0.000 1.235 2.201
Liz Lange for Target 1.8503 0.484 3.826 0.000 0.902 2.798
Loeffler Randall 2.9181 0.645 4.527 0.000 1.655 4.182
Logitech 1.9008 0.290 6.548 0.000 1.332 2.470
Lokai 1.8646 0.262 7.122 0.000 1.351 2.378
Lola 2.1752 0.493 4.412 0.000 1.209 3.141
Lolita 1.9654 0.412 4.765 0.000 1.157 2.774
London Fog 2.0282 0.327 6.207 0.000 1.388 2.669
London Jeans 1.3509 0.655 2.063 0.039 0.068 2.634
London Times 1.7048 0.411 4.151 0.000 0.900 2.510
Longaberger 2.0971 0.283 7.412 0.000 1.543 2.652
Longchamp 2.3492 0.411 5.721 0.000 1.544 3.154
Longchamp Paris 2.4664 0.345 7.143 0.000 1.790 3.143
Looney Tunes 2.1652 0.645 3.357 0.001 0.901 3.429
Lorac 2.3397 0.225 10.380 0.000 1.898 2.782
Lorna Jane 1.8067 0.644 2.804 0.005 0.544 3.070
Lou & Grey 3.686e-15 9.38e-15 0.393 0.694 -1.47e-14 2.21e-14
Loudmouth Golf 2.6685 0.650 4.106 0.000 1.395 3.942
Louis Vuitton 3.6898 0.216 17.068 0.000 3.266 4.114
Louisville Slugger 2.6071 0.424 6.148 0.000 1.776 3.438
Loungefly 2.0765 0.480 4.322 0.000 1.135 3.018
Love Culture 1.4715 0.646 2.277 0.023 0.205 2.738
Love Riche 1.7768 0.644 2.757 0.006 0.514 3.040
Lovers + Friends -1.835e-16 6.27e-15 -0.029 0.977 -1.25e-14 1.21e-14
LuLaRoe 2.2908 0.213 10.779 0.000 1.874 2.707
LucasArts Entertainment 1.2692 0.645 1.969 0.049 0.006 2.533
Lucca -9.249e-15 9.15e-15 -1.011 0.312 -2.72e-14 8.68e-15
Lucky Brand 2.0887 0.222 9.416 0.000 1.654 2.523
Lucky Brand Jeans 2.2776 0.411 5.545 0.000 1.473 3.083
Lucy Activewear 1.7924 0.327 5.487 0.000 1.152 2.433
Luli Fama 2.9628 0.645 4.596 0.000 1.699 4.226
Lulu 1.4920 0.645 2.315 0.021 0.229 2.755
Lulu Townsend 1.7602 0.645 2.729 0.006 0.496 3.024
Lulu's 1.9681 0.346 5.696 0.000 1.291 2.645
Lululemon 2.7701 0.213 13.006 0.000 2.353 3.188
LulyBoo 1.3507 0.344 3.927 0.000 0.677 2.025
Lunaire 1.5573 0.644 2.416 0.016 0.294 2.821
Lush 2.1397 0.226 9.487 0.000 1.698 2.582
Luvable Friends 1.3281 0.668 1.988 0.047 0.019 2.638
Luvs 1.6390 0.427 3.837 0.000 0.802 2.476
Lux 1.4223 0.481 2.959 0.003 0.480 2.365
Lysol 1.8038 0.376 4.798 0.000 1.067 2.541
M 2.4908 0.303 8.231 0.000 1.898 3.084
M.I.A. 1.9796 0.302 6.548 0.000 1.387 2.572
MAC 2.3491 0.215 10.948 0.000 1.929 2.770
MAC Cosmetics 2.4105 0.480 5.020 0.000 1.469 3.352
MAKE UP FOR EVER 1.8823 0.264 7.130 0.000 1.365 2.400
MAM Baby 2.3253 0.379 6.137 0.000 1.583 3.068
MARC BY MARC JACOBS 2.7808 0.235 11.848 0.000 2.321 3.241
MARC JACOBS 2.4818 0.221 11.233 0.000 2.049 2.915
MCM 4.2427 0.345 12.284 0.000 3.566 4.920
MCM Worldwide 3.3061 0.327 10.113 0.000 2.665 3.947
MEK 3.3071 0.481 6.878 0.000 2.365 4.250
MGA Entertainment 1.8335 0.258 7.114 0.000 1.328 2.339
MIA 1.7368 0.411 4.231 0.000 0.932 2.541
MICHELE 3.8556 0.645 5.978 0.000 2.591 5.120
MICHI by Michelle Watson 1.6391 0.481 3.406 0.001 0.696 2.582
MINIMALE ANIMALE 2.6429 0.645 4.100 0.000 1.380 3.906
MINKPINK 2.7187 0.481 5.656 0.000 1.777 3.661
MIU MIU 2.2436 0.480 4.673 0.000 1.303 3.185
MIkoh 2.9045 0.411 7.063 0.000 2.098 3.711
MLB 1.9302 0.238 8.118 0.000 1.464 2.396
MSI 2.0884 0.677 3.085 0.002 0.762 3.415
MSK 2.1688 0.644 3.365 0.001 0.906 3.432
Maaji 2.4688 0.480 5.145 0.000 1.528 3.409
Mac Cosmetic 2.3973 0.236 10.168 0.000 1.935 2.859
Mac Duggal 2.6818 0.644 4.161 0.000 1.419 3.945
Machine 1.6544 0.328 5.051 0.000 1.012 2.296
Maclaren 1.5730 0.679 2.317 0.021 0.242 2.904
Macy's 2.4680 0.345 7.148 0.000 1.791 3.145
Madame Alexander 1.8513 0.481 3.853 0.000 0.909 2.793
Madden Girl 1.7295 0.327 5.291 0.000 1.089 2.370
Madewell 2.5249 0.252 10.010 0.000 2.030 3.019
Madison 2.1566 0.645 3.343 0.001 0.892 3.421
Maeve 1.4303 0.480 2.981 0.003 0.490 2.371
Magazine 1.6103 0.645 2.495 0.013 0.345 2.875
Magellan® 1.6801 0.417 4.031 0.000 0.863 2.497
Maggie Barnes -2.27e-14 1.05e-14 -2.168 0.030 -4.32e-14 -2.18e-15
Magic Bullet 2.0724 0.299 6.932 0.000 1.486 2.658
Magnavox 1.5552 0.422 3.685 0.000 0.728 2.382
Maidenform 2.2011 0.345 6.376 0.000 1.524 2.878
Maison Goyard 3.272e-15 7.55e-15 0.433 0.665 -1.15e-14 1.81e-14
Maison Martin Margiela 3.8759 0.645 6.011 0.000 2.612 5.140
Maisto 2.3745 0.486 4.887 0.000 1.422 3.327
Majestic 2.2863 0.260 8.796 0.000 1.777 2.796
Makeup Geek 2.0114 0.644 3.121 0.002 0.748 3.275
Mally Beauty 2.5394 0.410 6.188 0.000 1.735 3.344
Mamia 2.6801 0.644 4.159 0.000 1.417 3.943
Manduka 2.8036 0.384 7.301 0.000 2.051 3.556
Mango 1.7253 0.346 4.990 0.000 1.048 2.403
Manhattan Toy 1.5874 0.647 2.454 0.014 0.319 2.855
Manic Panic 1.8722 0.384 4.881 0.000 1.120 2.624
Manolo Blahnik 3.2454 0.480 6.755 0.000 2.304 4.187
Marc Ecko 1.323e-14 9.52e-15 1.390 0.165 -5.42e-15 3.19e-14
Marc Fisher 2.5293 0.645 3.924 0.000 1.266 3.793
Marc New York 1.7766 0.480 3.700 0.000 0.836 2.718
Marciano 2.1910 0.410 5.339 0.000 1.387 2.995
Margaritaville 1.5925 0.645 2.470 0.014 0.329 2.856
Marika 1.8169 0.371 4.899 0.000 1.090 2.544
Marimekko -1.007e-14 9.15e-15 -1.100 0.271 -2.8e-14 7.87e-15
Marineland -1.226e-15 8.32e-15 -0.147 0.883 -1.75e-14 1.51e-14
Mario Badescu Skin Care 1.5675 0.645 2.432 0.015 0.304 2.831
Marmot 2.1972 0.372 5.913 0.000 1.469 2.926
Marni 2.6459 0.644 4.106 0.000 1.383 3.909
Martha Stewart 1.5822 0.331 4.778 0.000 0.933 2.231
Marucci -2.326e-15 7.83e-15 -0.297 0.766 -1.77e-14 1.3e-14
Marvel 2.0140 0.230 8.743 0.000 1.562 2.465
Marvel Universe 1.097e-14 7.49e-15 1.465 0.143 -3.71e-15 2.57e-14
Mary Kay 2.0720 0.217 9.538 0.000 1.646 2.498
Masquerade 7.056e-15 9.51e-15 0.742 0.458 -1.16e-14 2.57e-14
Masters 2.5636 0.480 5.339 0.000 1.622 3.505
Matchbox 2.3754 0.414 5.740 0.000 1.564 3.186
Material Girl 1.3425 0.302 4.440 0.000 0.750 1.935
Mattel® 2.0296 0.220 9.234 0.000 1.599 2.460
Maui & Sons 3.0198 0.645 4.679 0.000 1.755 4.285
Maurices 1.7141 0.219 7.813 0.000 1.284 2.144
Max Factor 1.0872 0.480 2.267 0.023 0.147 2.027
Max Studio 2.1155 0.313 6.757 0.000 1.502 2.729
Maxi-Cosi 2.5846 0.438 5.903 0.000 1.727 3.443
Maybelline 1.5253 0.219 6.958 0.000 1.096 1.955
McDavid 2.2343 0.645 3.463 0.001 0.970 3.499
McFarlane Sports -1.028e-14 7.94e-15 -1.295 0.195 -2.58e-14 5.28e-15
McFarlane Toys 2.4968 0.411 6.077 0.000 1.692 3.302
Medela 2.3240 0.250 9.280 0.000 1.833 2.815
Mega Bloks 1.7555 0.300 5.846 0.000 1.167 2.344
Mega Hobby 2.243e-14 8.28e-15 2.709 0.007 6.2e-15 3.87e-14
Mel by Melissa 2.5486 0.645 3.954 0.000 1.285 3.812
Melissa 1.071e-14 9.07e-15 1.181 0.238 -7.06e-15 2.85e-14
Melissa & Doug 1.9999 0.270 7.417 0.000 1.471 2.528
Melt Cosmetics 2.2980 0.644 3.566 0.000 1.035 3.561
Members Only 1.983e-15 9.22e-15 0.215 0.830 -1.61e-14 2e-14
Meme 3.0469 0.644 4.728 0.000 1.784 4.310
Merle Norman 1.9168 0.410 4.670 0.000 1.112 2.721
Mermaid Maternity 1.6610 0.645 2.573 0.010 0.396 2.926
Merona 1.5487 0.222 6.968 0.000 1.113 1.984
Merrell 1.7135 0.264 6.485 0.000 1.196 2.231
Metal Mulisha 2.0678 0.261 7.920 0.000 1.556 2.579
Metaphor 1.1969 0.645 1.857 0.063 -0.067 2.461
Mezco 1.9857 0.645 3.076 0.002 0.721 3.251
Michael Kors 2.8441 0.213 13.347 0.000 2.426 3.262
Michael Stars 1.8487 0.371 4.977 0.000 1.121 2.577
Miche 8.737e-15 7.4e-15 1.180 0.238 -5.77e-15 2.32e-14
Micro Innovations 2.3251 0.645 3.606 0.000 1.061 3.589
Microsoft 2.2030 0.229 9.614 0.000 1.754 2.652
Middleton Doll Company 2.7998 0.372 7.533 0.000 2.071 3.528
Mighty Fine 1.7546 0.645 2.722 0.006 0.491 3.018
Mikasa 2.8474 0.487 5.852 0.000 1.894 3.801
Mikimoto 4.4277 0.644 6.870 0.000 3.165 5.691
Milani 1.6739 0.226 7.393 0.000 1.230 2.118
Milani Cosmetics -4.852e-15 8.13e-15 -0.597 0.551 -2.08e-14 1.11e-14
Miley Cyrus 1.6312 0.411 3.971 0.000 0.826 2.436
Millau 2.3387 0.644 3.629 0.000 1.076 3.602
Miller 1.7028 0.645 2.642 0.008 0.439 2.966
Milly 2.6729 0.480 5.571 0.000 1.733 3.613
Mimi 1.9255 0.480 4.012 0.000 0.985 2.866
Mini Melissa 3.1785 0.480 6.622 0.000 2.238 4.119
Minnetonka 1.9997 0.345 5.792 0.000 1.323 2.676
Miraclesuit® 1.9152 0.480 3.989 0.000 0.974 2.856
Mirro -3.384e-16 7.89e-15 -0.043 0.966 -1.58e-14 1.51e-14
Misfits 2.5273 0.647 3.908 0.000 1.260 3.795
Miss Chievous 2.7824 0.644 4.317 0.000 1.519 4.046
Miss Me 2.5856 0.217 11.907 0.000 2.160 3.011
Missguided 2.1153 0.330 6.406 0.000 1.468 2.763
Mission -3.221e-15 6.65e-15 -0.485 0.628 -1.63e-14 9.81e-15
Missoni 1.6787 0.480 3.496 0.000 0.738 2.620
Mitchell & Ness 1.9974 0.268 7.455 0.000 1.472 2.522
Mitchum 1.3637 0.646 2.112 0.035 0.098 2.629
Mitsubishi 2.4209 0.654 3.701 0.000 1.139 3.703
Mixit 1.2756 0.480 2.659 0.008 0.335 2.216
Mizuno 2.0914 0.275 7.614 0.000 1.553 2.630
Moa Moa 1.7723 0.645 2.749 0.006 0.509 3.036
Moda International 1.5329 0.373 4.109 0.000 0.802 2.264
Modcloth 2.0949 0.281 7.464 0.000 1.545 2.645
Modern Amusement 2.5010 0.650 3.849 0.000 1.228 3.775
Moncler 3.8808 0.412 9.431 0.000 3.074 4.687
Mongoose 1.54e-15 8.25e-15 0.187 0.852 -1.46e-14 1.77e-14
Monistat 1.6242 0.646 2.516 0.012 0.359 2.890
Monopoly 1.8599 0.354 5.255 0.000 1.166 2.554
Monoreno 1.5154 0.645 2.350 0.019 0.252 2.779
Monster Cable 4.325e-15 7.8e-15 0.555 0.579 -1.1e-14 1.96e-14
Monteau 1.6312 0.411 3.970 0.000 0.826 2.437
Moon Boot 1.0538 0.644 1.635 0.102 -0.209 2.317
Moose Toys 1.8553 0.231 8.043 0.000 1.403 2.307
Mopar -6.64e-15 6.62e-15 -1.003 0.316 -1.96e-14 6.33e-15
Mopas 1.6095 0.644 2.498 0.012 0.347 2.872
Morgan & Co. -1.797e-14 9.41e-15 -1.909 0.056 -3.64e-14 4.79e-16
Morphe Cosmetics 2.3649 0.226 10.442 0.000 1.921 2.809
Moschino 3.2353 0.658 4.918 0.000 1.946 4.525
Mossimo 1.5450 0.218 7.090 0.000 1.118 1.972
Mossimo Supply Co. 1.5318 0.246 6.230 0.000 1.050 2.014
Mossy Oak 1.7830 0.294 6.066 0.000 1.207 2.359
Motherhood 1.3816 0.482 2.869 0.004 0.438 2.325
Motherhood Maternity 1.9044 0.233 8.187 0.000 1.449 2.360
Motorcycle Stuff 1.6974 0.540 3.143 0.002 0.639 2.756
Motorola 2.3197 0.349 6.655 0.000 1.636 3.003
Moulinette Soeurs 1.8668 0.645 2.896 0.004 0.604 3.130
Mountain Hardwear 1.7096 0.480 3.559 0.000 0.768 2.651
Mountain Lake 1.7931 0.646 2.774 0.006 0.526 3.060
Movado 3.3263 0.480 6.923 0.000 2.385 4.268
Moving Comfort 2.1545 0.411 5.242 0.000 1.349 2.960
Mr. Coffee 2.2117 0.423 5.226 0.000 1.382 3.041
Mud Pie 2.1922 0.645 3.398 0.001 0.928 3.457
Mudd 1.4451 0.239 6.052 0.000 0.977 1.913
Muk Luks 1.5538 0.646 2.404 0.016 0.287 2.820
Mulberry 1.4740 0.646 2.283 0.022 0.208 2.739
Munchkin 1.6676 0.264 6.323 0.000 1.151 2.185
Murad 1.8640 0.480 3.884 0.000 0.923 2.805
Muse 2.1108 0.651 3.241 0.001 0.834 3.387
My Brest Friend 2.2538 0.661 3.411 0.001 0.959 3.549
My Little Pony 1.9186 0.248 7.735 0.000 1.432 2.405
My Michelle 1.3860 0.347 3.991 0.000 0.705 2.067
Mylec -1.553e-14 9.4e-15 -1.653 0.098 -3.39e-14 2.89e-15
Mystic 1.1612 0.480 2.420 0.016 0.221 2.102
NASCAR 1.2678 0.481 2.637 0.008 0.325 2.210
NBA 1.7039 0.236 7.215 0.000 1.241 2.167
NCAA 2.5726 0.647 3.976 0.000 1.304 3.841
NECA 2.5873 0.411 6.292 0.000 1.781 3.393
NFL 1.8199 0.221 8.249 0.000 1.387 2.252
NHL 2.0810 0.327 6.363 0.000 1.440 2.722
NIC+ZOE 1.8231 0.480 3.800 0.000 0.883 2.763
NYC 1.2643 0.644 1.962 0.050 0.001 2.527
NYDJ 1.9619 0.645 3.043 0.002 0.698 3.225
NYX 1.7447 0.217 8.058 0.000 1.320 2.169
Naked Zebra 2.1409 0.644 3.322 0.001 0.878 3.404
Nana 1.9428 0.644 3.015 0.003 0.680 3.206
Nanette Lepore 1.5824 0.656 2.412 0.016 0.297 2.868
Nars 2.2936 0.223 10.275 0.000 1.856 2.731
Nasty Gal 2.1162 0.236 8.976 0.000 1.654 2.578
Native 2.7240 0.345 7.889 0.000 2.047 3.401
Naturalizer 1.8306 0.645 2.837 0.005 0.566 3.095
Nautica 1.7816 0.254 7.005 0.000 1.283 2.280
Necessary Clothing 1.8209 0.644 2.825 0.005 0.558 3.084
Neff 1.9879 0.371 5.353 0.000 1.260 2.716
Neff Headwear 1.9109 0.645 2.964 0.003 0.647 3.175
Neiman Marcus 2.3324 0.371 6.286 0.000 1.605 3.060
Nerf 2.0764 0.271 7.654 0.000 1.545 2.608
Nespresso 2.8086 0.411 6.827 0.000 2.002 3.615
Netgear 2.1179 0.325 6.513 0.000 1.481 2.755
Neutrogena 1.7336 0.262 6.615 0.000 1.220 2.247
New Balance 1.8090 0.228 7.940 0.000 1.362 2.256
New Directions 1.9379 0.287 6.758 0.000 1.376 2.500
New Era 2.2420 0.232 9.647 0.000 1.786 2.698
New Look -5.541e-15 1.01e-14 -0.548 0.583 -2.53e-14 1.43e-14
New York & Company 1.6920 0.226 7.483 0.000 1.249 2.135
New York Color -5.957e-15 8.87e-15 -0.672 0.502 -2.33e-14 1.14e-14
Newport News 1.6596 0.480 3.457 0.001 0.719 2.600
Next 1.5219 0.480 3.172 0.002 0.581 2.462
Next Level 1.6321 0.480 3.402 0.001 0.692 2.572
Next Level Apparel 1.6678 0.371 4.496 0.000 0.941 2.395
Nick & Nora 1.8170 0.481 3.778 0.000 0.874 2.760
Nick Jr. 1.8883 0.313 6.024 0.000 1.274 2.503
Nickelodeon 1.7953 0.237 7.578 0.000 1.331 2.260
Nicole Lee -8.257e-15 6.92e-15 -1.194 0.233 -2.18e-14 5.3e-15
Nicole Miller 1.6622 0.371 4.478 0.000 0.935 2.390
Nicole by Nicole Miller 1.6360 0.480 3.410 0.001 0.696 2.576
Nike 2.0855 0.212 9.820 0.000 1.669 2.502
Nike Golf 1.8655 0.364 5.120 0.000 1.151 2.580
Nikon 2.5133 0.253 9.927 0.000 2.017 3.009
Nina 1.7742 0.645 2.751 0.006 0.510 3.038
Nine West 1.7351 0.229 7.577 0.000 1.286 2.184
Ninja 2.3432 0.352 6.656 0.000 1.653 3.033
Nintendo 2.1682 0.214 10.130 0.000 1.749 2.588
Nivea 1.8121 0.294 6.157 0.000 1.235 2.389
Nixon 2.5955 0.295 8.790 0.000 2.017 3.174
No Boundaries 1.3898 0.232 5.990 0.000 0.935 1.844
No7 Make-up 1.6142 0.644 2.505 0.012 0.351 2.877
NoJo 2.2205 0.378 5.874 0.000 1.480 2.961
Nobo 0.9014 0.480 1.878 0.060 -0.039 1.842
Nokia 1.7724 0.480 3.692 0.000 0.832 2.713
Nollie 1.6924 0.302 5.601 0.000 1.100 2.285
Nordstrom 2.2461 0.226 9.926 0.000 1.803 2.690
Nordstrom Baby 9.855e-15 1.03e-14 0.960 0.337 -1.03e-14 3e-14
Norelco 3.0918 0.481 6.422 0.000 2.148 4.036
Nostalgia 2.0531 0.646 3.179 0.001 0.787 3.319
Nostalgia Electrics 2.5981 0.377 6.900 0.000 1.860 3.336
Not Rated 1.671e-15 9.05e-15 0.185 0.853 -1.61e-14 1.94e-14
Notations 1.9543 0.371 5.266 0.000 1.227 2.682
Novella Royale 2.164e-14 8.77e-15 2.469 0.014 4.46e-15 3.88e-14
Nuby 1.6049 0.351 4.573 0.000 0.917 2.293
Nuk 1.5368 0.337 4.565 0.000 0.877 2.197
Nursery Fresh 2.1129 0.900 2.347 0.019 0.348 3.877
Nylabone 1.6359 0.646 2.531 0.011 0.369 2.903
O'Neill 1.7398 0.287 6.061 0.000 1.177 2.302
O.B. 2.0246 0.481 4.205 0.000 1.081 2.968
OGIO® 1.6318 0.647 2.522 0.012 0.364 2.900
OGX 1.601e-14 1.08e-14 1.478 0.139 -5.22e-15 3.73e-14
OPI 2.0115 0.481 4.186 0.000 1.070 2.953
Oakley 2.6251 0.231 11.359 0.000 2.172 3.078
Obagi 1.8442 0.644 2.862 0.004 0.581 3.107
Obey 2.1100 0.281 7.516 0.000 1.560 2.660
Obey Clothing 1.8558 0.302 6.139 0.000 1.263 2.448
Ocean Current 1.7152 0.654 2.622 0.009 0.433 2.997
Ocean Drive 2.4946 0.644 3.871 0.000 1.231 3.758
Ocean Pacific 1.5840 0.480 3.301 0.001 0.643 2.525
Odyssey 0.5212 0.688 0.758 0.449 -0.827 1.869
Off-White 3.3112 0.345 9.587 0.000 2.634 3.988
Office Depot 2.0541 0.249 8.262 0.000 1.567 2.541
Oh Baby by Motherhood 1.9145 0.412 4.642 0.000 1.106 2.723
Okie Dokie 1.8597 0.645 2.884 0.004 0.596 3.123
Olay 1.7877 0.281 6.370 0.000 1.238 2.338
Old Navy 1.6394 0.214 7.671 0.000 1.221 2.058
Old Spice 0.6452 0.646 0.999 0.318 -0.620 1.911
Old Varsity Brand 1.207e-15 7.62e-15 0.158 0.874 -1.37e-14 1.61e-14
Ole Henricksen 2.3538 0.644 3.652 0.000 1.091 3.617
Olga 1.8263 0.480 3.806 0.000 0.886 2.767
Olive & Oak 1.6149 0.481 3.355 0.001 0.671 2.558
Oliver Peoples 2.5102 0.645 3.892 0.000 1.246 3.774
Olivia + Joy 1.8821 0.646 2.912 0.004 0.615 3.149
Olsenboye 1.6709 0.644 2.593 0.010 0.408 2.934
OluKai 2.5090 0.645 3.890 0.000 1.245 3.773
Omega 2.4966 0.653 3.826 0.000 1.218 3.776
On the Byas 1.9303 0.346 5.577 0.000 1.252 2.609
On-Stage Stands 2.7679 0.665 4.162 0.000 1.465 4.071
One Clothing 1.7206 0.481 3.577 0.000 0.778 2.664
One Step Up -1.429e-14 5.89e-15 -2.426 0.015 -2.58e-14 -2.74e-15
One Teaspoon 3.2149 0.313 10.264 0.000 2.601 3.829
One World 1.9820 0.371 5.340 0.000 1.254 2.710
Only Hearts 1.6573 0.644 2.572 0.010 0.394 2.920
Onzie 7.474e-15 9.24e-15 0.809 0.418 -1.06e-14 2.56e-14
Op 1.5627 0.264 5.912 0.000 1.045 2.081
Opti-Free 1.6260 0.646 2.518 0.012 0.361 2.891
Oral B 2.2582 0.348 6.498 0.000 1.577 2.939
Orbit Baby 3.5671 0.387 9.207 0.000 2.808 4.326
Origins 1.9526 0.271 7.195 0.000 1.421 2.485
Orla Kiely 2.2547 0.645 3.493 0.000 0.990 3.520
Oscar de la Renta 1.5528 0.645 2.409 0.016 0.289 2.816
Osh Kosh B'gosh 1.6188 0.224 7.220 0.000 1.179 2.058
Osprey 1.6580 0.645 2.571 0.010 0.394 2.922
Oster 2.0757 0.486 4.268 0.000 1.123 3.029
OtterBox 2.7230 0.411 6.632 0.000 1.918 3.528
Ouidad 2.4935 0.646 3.862 0.000 1.228 3.759
Outward Hound 2.4534 0.482 5.087 0.000 1.508 3.399
Oxford Golf 1.9254 0.646 2.979 0.003 0.659 3.192
PANDORA 2.9470 0.217 13.550 0.000 2.521 3.373
PASS PORT -6.644e-15 8.55e-15 -0.777 0.437 -2.34e-14 1.01e-14
PBS 3.3162 0.645 5.141 0.000 2.052 4.581
PEZ 0.5013 0.645 0.777 0.437 -0.763 1.765
PING 1.3422 0.688 1.952 0.051 -0.006 2.690
PINK 2.2110 0.212 10.414 0.000 1.795 2.627
PJ Salvage 2.1409 0.644 3.322 0.001 0.878 3.404
PNY 1.5078 0.485 3.112 0.002 0.558 2.457
POND'S 2.1403 0.644 3.321 0.001 0.877 3.403
PONY -1.1e-14 8.13e-15 -1.353 0.176 -2.69e-14 4.94e-15
PROPPER 0.3787 0.646 0.586 0.558 -0.888 1.646
PRPS 2.5152 0.648 3.880 0.000 1.245 3.786
PUMA 2.1942 0.218 10.048 0.000 1.766 2.622
Pacific Sunwear 1.9295 0.230 8.390 0.000 1.479 2.380
Paige Denim 2.5058 0.327 7.654 0.000 1.864 3.147
Pampered Chef 2.2217 0.248 8.949 0.000 1.735 2.708
Pampers 2.0008 0.296 6.756 0.000 1.420 2.581
Pampers Baby Dry 2.1060 0.389 5.409 0.000 1.343 2.869
Pampers Easy Ups 1.4961 0.655 2.284 0.022 0.212 2.780
Pampers Swaddlers 1.5731 0.325 4.846 0.000 0.937 2.209
Panache 1.9882 0.644 3.085 0.002 0.725 3.251
Panasonic 1.8205 0.496 3.668 0.000 0.848 2.793
Pantene 2.5098 0.481 5.217 0.000 1.567 3.453
Papaya 1.6227 0.261 6.214 0.000 1.111 2.134
Paper Mate 1.8972 0.298 6.376 0.000 1.314 2.480
Papillon 2.6459 0.644 4.105 0.000 1.383 3.909
Paradise Collection 3.4531 0.486 7.106 0.000 2.501 4.406
Paris Blues 1.0622 0.482 2.206 0.027 0.118 2.006
Park Designs 2.4514 0.654 3.747 0.000 1.169 3.734
Park Lane -1.144e-14 8.73e-15 -1.311 0.190 -2.85e-14 5.67e-15
PartyLite 1.8221 0.291 6.258 0.000 1.251 2.393
Passport 5.783e-15 6.62e-15 0.874 0.382 -7.18e-15 1.88e-14
Patagonia, Inc. 2.2340 0.371 6.021 0.000 1.507 2.961
Patricia Nash 2.4109 0.481 5.016 0.000 1.469 3.353
Paul Frank 1.3770 0.644 2.137 0.033 0.114 2.640
Peaches 1.9893 0.645 3.085 0.002 0.725 3.253
Peanuts 1.4739 0.480 3.070 0.002 0.533 2.415
Pearhead 2.8791 0.645 4.464 0.000 1.615 4.143
Pearl Izumi 1.4669 0.646 2.272 0.023 0.202 2.732
Peck & Peck 2.3379 0.412 5.674 0.000 1.530 3.146
Pediasure 1.789e-16 7.95e-15 0.022 0.982 -1.54e-14 1.58e-14
Pedigree 1.4634 0.648 2.259 0.024 0.194 2.733
Pendleton 2.1098 0.372 5.670 0.000 1.380 2.839
Peppa Pig 1.6332 0.645 2.532 0.011 0.369 2.897
Perler 7.651e-15 7.03e-15 1.089 0.276 -6.13e-15 2.14e-14
Perry Ellis 2.0397 0.411 4.958 0.000 1.233 2.846
PetSafe 2.8228 0.482 5.853 0.000 1.878 3.768
PetSmart 1.9956 0.256 7.797 0.000 1.494 2.497
Peter Millar 2.7625 0.646 4.273 0.000 1.495 4.030
Petit Tresor 1.8769 0.662 2.834 0.005 0.579 3.175
Petmate 3.0207 0.482 6.263 0.000 2.075 3.966
Pets First 2.7451 0.482 5.701 0.000 1.801 3.689
Pfaltzgraff 1.7337 0.480 3.611 0.000 0.793 2.675
Phat Farm/Baby Phat 1.621e-14 7.36e-15 2.203 0.028 1.79e-15 3.06e-14
Philips 2.7624 0.288 9.579 0.000 2.197 3.328
Phillips 1.8407 0.375 4.908 0.000 1.106 2.576
Philosophy 2.3591 0.371 6.361 0.000 1.632 3.086
Physicians Formula 1.6114 0.238 6.774 0.000 1.145 2.078
Physicians Prefer 2.9144 0.649 4.491 0.000 1.642 4.186
Piedmont -1.587e-14 8.18e-15 -1.941 0.052 -3.19e-14 1.55e-16
Pier One 1.9325 0.294 6.572 0.000 1.356 2.509
Pierre Dumas 8.706e-15 8.62e-15 1.010 0.312 -8.19e-15 2.56e-14
Pillow Pets 1.8421 0.372 4.949 0.000 1.112 2.572
Pilot 1.5889 0.486 3.269 0.001 0.636 2.541
Pineapple Connection 2.5278 0.645 3.922 0.000 1.264 3.791
Pink Lotus 6.085e-15 6.69e-15 0.910 0.363 -7.02e-15 1.92e-14
Pinkblush 1.9662 0.374 5.258 0.000 1.233 2.699
Pioneer 3.2354 0.616 5.255 0.000 2.029 4.442
Piperlime 1.444e-14 8.68e-15 1.664 0.096 -2.57e-15 3.15e-14
Planet Motherhood 1.7289 0.650 2.660 0.008 0.455 3.003
Plantronics 2.6460 0.483 5.482 0.000 1.700 3.592
Play Along 1.4767 0.645 2.289 0.022 0.212 2.741
Play-Doh 2.0277 0.346 5.853 0.000 1.349 2.707
PlayStation 1.1025 0.411 2.682 0.007 0.297 1.908
Playhut 2.7200 0.647 4.205 0.000 1.452 3.988
Playmates 2.4080 0.411 5.856 0.000 1.602 3.214
Playmobil 2.3013 0.314 7.319 0.000 1.685 2.918
Playskool 1.7411 0.347 5.016 0.000 1.061 2.421
Playtex 1.8897 0.265 7.131 0.000 1.370 2.409
Pleasure Party 3.133e-15 9.01e-15 0.348 0.728 -1.45e-14 2.08e-14
Pleione 1.4660 0.644 2.275 0.023 0.203 2.729
Plugg 1.5091 0.650 2.323 0.020 0.236 2.783
Plus White 7.695e-16 8.72e-15 0.088 0.930 -1.63e-14 1.79e-14
PoGo! Products 3.5063 0.483 7.253 0.000 2.559 4.454
Poetry 1.1370 0.644 1.764 0.078 -0.126 2.400
Poise 2.5697 0.481 5.337 0.000 1.626 3.513
Pokemon 2.0561 0.221 9.323 0.000 1.624 2.488
Pokemon USA 1.8506 0.320 5.787 0.000 1.224 2.477
Polar -5.02e-15 5.58e-15 -0.900 0.368 -1.59e-14 5.91e-15
Polaroid 1.0205 0.650 1.571 0.116 -0.253 2.294
Poligrip 2.1746 0.646 3.368 0.001 0.909 3.440
Polo Jeans Co. 2.2804 0.645 3.538 0.000 1.017 3.544
Polo Ralph Lauren 2.1539 0.215 10.005 0.000 1.732 2.576
Polycom 1.9830 0.647 3.065 0.002 0.715 3.251
Pop Beauty 1.18e-14 8.02e-15 1.472 0.141 -3.91e-15 2.75e-14
PopSockets 1.6223 0.224 7.236 0.000 1.183 2.062
Popular 0.7487 0.648 1.155 0.248 -0.522 2.019
Port & Company 1.7892 0.644 2.777 0.005 0.526 3.052
Positive Attitude 2.924e-14 9.77e-15 2.994 0.003 1.01e-14 4.84e-14
Post-it 1.6296 0.418 3.900 0.000 0.811 2.449
Pottery Barn 2.2083 0.262 8.419 0.000 1.694 2.722
Pottery Barn Kids 1.9597 0.387 5.061 0.000 1.201 2.719
Power A 2.0556 0.648 3.172 0.002 0.785 3.326
Prada 3.1242 0.240 12.992 0.000 2.653 3.595
Precious Moments 2.1436 0.415 5.164 0.000 1.330 2.957
Premier Designs 2.5020 0.275 9.083 0.000 1.962 3.042
Prescriptives 3.63e-15 8.86e-15 0.410 0.682 -1.37e-14 2.1e-14
Presto 2.3235 0.379 6.127 0.000 1.580 3.067
Pretty Angel 1.4076 0.645 2.183 0.029 0.144 2.671
Prevue Pet Products 6.65e-15 7.31e-15 0.909 0.363 -7.69e-15 2.1e-14
Primark 1.5999 0.384 4.171 0.000 0.848 2.352
Primitive -3.643e-15 8.02e-15 -0.454 0.650 -1.94e-14 1.21e-14
Primula 1.8047 0.645 2.798 0.005 0.541 3.069
Prince LionHeart 2.1094 0.668 3.157 0.002 0.800 3.419
Pringle of Scotland 2.4936 0.648 3.851 0.000 1.224 3.763
Pro Keds 1.3819 0.411 3.364 0.001 0.577 2.187
Pro Player 3.374e-15 6.98e-15 0.483 0.629 -1.03e-14 1.71e-14
ProClub 2.8826 0.645 4.472 0.000 1.619 4.146
Proactiv 1.2889 0.480 2.683 0.007 0.347 2.231
Proctor Silex 1.8402 0.495 3.714 0.000 0.869 2.811
Proenza Schouler 5.0983 0.646 7.889 0.000 3.832 6.365
Profile by Gottex 1.4901 0.645 2.309 0.021 0.225 2.755
Pull&Bear -1.502e-14 6.99e-15 -2.149 0.032 -2.87e-14 -1.32e-15
Pur Minerals 2.0952 0.271 7.728 0.000 1.564 2.627
Pur-lisse 0.5885 0.644 0.913 0.361 -0.675 1.852
Pure Boxing 1.445e-14 8.42e-15 1.716 0.086 -2.05e-15 3.09e-14
Purina 2.2407 0.482 4.646 0.000 1.295 3.186
Purina Beneful 1.6191 0.482 3.357 0.001 0.674 2.564
Pyrex 2.3101 0.233 9.915 0.000 1.853 2.767
QALO 2.1719 0.645 3.369 0.001 0.908 3.435
Quacker Factory 1.4112 0.645 2.189 0.029 0.148 2.675
Quay Australia 2.7810 0.282 9.865 0.000 2.228 3.334
Quiksilver 2.0333 0.348 5.842 0.000 1.351 2.715
Qupid 1.7034 0.345 4.936 0.000 1.027 2.380
R & M Richards 1.2983 0.645 2.013 0.044 0.034 2.563
RACHEL PALLY 2.8183 0.644 4.373 0.000 1.555 4.081
RCA 1.5960 0.647 2.467 0.014 0.328 2.864
REI 2.2386 0.314 7.138 0.000 1.624 2.853
RIM 2.4513 0.480 5.102 0.000 1.510 3.393
ROMWE 1.5839 0.480 3.299 0.001 0.643 2.525
RVCA 2.0875 0.294 7.090 0.000 1.510 2.665
Rachael Ray 2.1369 0.330 6.478 0.000 1.490 2.783
Rachel Roy 2.4138 0.480 5.032 0.000 1.474 3.354
Rachel Zoe 2.3343 0.645 3.621 0.000 1.071 3.598
Radio Flyer 1.8279 0.777 2.352 0.019 0.305 3.351
Rae Dunn 2.5148 0.214 11.752 0.000 2.095 2.934
Rafaella 1.6750 0.646 2.593 0.010 0.409 2.941
Rails 1.194e-14 6.37e-15 1.875 0.061 -5.42e-16 2.44e-14
Rainbow Shops 2.2460 0.371 6.052 0.000 1.519 2.973
Ralph Lauren 2.0273 0.215 9.413 0.000 1.605 2.449
Ralph Lauren Collection 1.6378 0.646 2.534 0.011 0.371 2.905
Rampage 1.7538 0.371 4.727 0.000 1.027 2.481
Rave 1.9352 0.281 6.899 0.000 1.385 2.485
Ravensburger 1.8141 0.491 3.698 0.000 0.853 2.776
Rawlings 2.9807 0.302 9.861 0.000 2.388 3.573
Ray-Ban 3.0248 0.220 13.731 0.000 2.593 3.457
Razer 2.6851 0.328 8.187 0.000 2.042 3.328
Razor 1.1774 0.663 1.775 0.076 -0.123 2.478
Real Techniques 2.9971 0.480 6.239 0.000 2.056 3.939
Realtree 1.6463 0.372 4.431 0.000 0.918 2.374
Rebecca Minkoff 2.7284 0.257 10.635 0.000 2.226 3.231
Rebecca Taylor 2.7783 0.644 4.311 0.000 1.515 4.041
Rebel Spirit 2.2411 0.644 3.477 0.001 0.978 3.504
Rebel Yell 2.1375 0.644 3.317 0.001 0.875 3.400
Red Camel 1.4643 0.644 2.272 0.023 0.201 2.727
Red Cherry 1.6218 0.302 5.364 0.000 1.029 2.214
Red Rivet Jeans 1.3865 0.645 2.151 0.031 0.123 2.650
Redken 2.3358 0.647 3.608 0.000 1.067 3.605
Reebok 1.8834 0.219 8.620 0.000 1.455 2.312
Reef 1.6732 0.294 5.692 0.000 1.097 2.249
Reference Point 1.6192 0.649 2.497 0.013 0.348 2.890
Reformation 2.9999 0.645 4.654 0.000 1.737 4.263
Refuge 1.5646 0.327 4.788 0.000 0.924 2.205
Reign 2.1197 0.480 4.415 0.000 1.179 3.061
Relativity 1.6172 0.645 2.508 0.012 0.353 2.881
Relic 2.0553 0.645 3.187 0.001 0.791 3.319
Remington 2.2273 0.481 4.626 0.000 1.284 3.171
Rene Rofe 5.431e-15 7.65e-15 0.710 0.478 -9.55e-15 2.04e-14
Resistol 2.1302 0.645 3.304 0.001 0.866 3.394
Retrofit 2.0999 0.665 3.159 0.002 0.797 3.403
Revere 1.575e-16 8.2e-15 0.019 0.985 -1.59e-14 1.62e-14
Revlon 1.6757 0.226 7.417 0.000 1.233 2.118
Rewind 1.5485 0.302 5.122 0.000 0.956 2.141
Rhapsody 0.7270 0.649 1.120 0.263 -0.545 1.999
Rhonda Shear 1.5966 0.645 2.476 0.013 0.333 2.860
Riddell 1.9664 0.332 5.922 0.000 1.316 2.617
Riders 1.3968 0.483 2.891 0.004 0.450 2.344
Riders by Lee 0.9046 0.644 1.404 0.160 -0.359 2.168
Right Guard 1.9810 0.646 3.068 0.002 0.716 3.246
Rihanna 3.3059 0.411 8.045 0.000 2.500 4.111
Rimmel 1.6651 0.327 5.097 0.000 1.025 2.305
Ring 2.3959 0.414 5.784 0.000 1.584 3.208
Ring of Fire 2.181e-14 9.55e-15 2.285 0.022 3.1e-15 4.05e-14
Rio Grande Games 1.8595 0.645 2.882 0.004 0.595 3.124
Rip Curl 2.1635 0.480 4.507 0.000 1.223 3.104
Ripcurl 1.7683 0.480 3.683 0.000 0.827 2.709
Ripple Junction 2.0672 0.371 5.566 0.000 1.339 2.795
Rival 0.8744 0.486 1.799 0.072 -0.078 1.827
River Island 2.3287 0.645 3.608 0.000 1.064 3.594
Roaman's 1.1478 0.645 1.780 0.075 -0.116 2.412
Robbie Bee -1.057e-14 8.92e-15 -1.184 0.236 -2.81e-14 6.92e-15
Robeez 1.6756 0.371 4.512 0.000 0.948 2.403
Robert Talbott 2.5197 0.656 3.840 0.000 1.234 3.806
Roberto Cavalli 3.1879 0.644 4.947 0.000 1.925 4.451
Robin's Jeans 2.8523 0.413 6.911 0.000 2.043 3.661
Rocawear 1.7237 0.313 5.505 0.000 1.110 2.337
Rock & Republic 1.9729 0.261 7.555 0.000 1.461 2.485
Rock & Roll Cowgirl 1.634e-14 7.98e-15 2.048 0.041 7.06e-16 3.2e-14
Rock Revival 3.0090 0.219 13.746 0.000 2.580 3.438
Rocket Dog 1.305e-14 9.69e-15 1.346 0.178 -5.95e-15 3.2e-14
Rodan and Fields 3.8497 0.645 5.971 0.000 2.586 5.113
Roku 1.8395 0.276 6.675 0.000 1.299 2.380
Rolex 3.3577 0.295 11.377 0.000 2.779 3.936
Roller Derby 1.4735 0.663 2.221 0.026 0.173 2.774
Rollerblade 1.8620 0.505 3.688 0.000 0.872 2.852
Romeo & Juliet Couture 1.3723 0.647 2.120 0.034 0.104 2.641
Ron Jon 1.8346 0.645 2.846 0.004 0.571 3.098
Ronni Nicole 1.7969 0.673 2.670 0.008 0.478 3.116
Roper 2.8316 0.644 4.393 0.000 1.568 4.095
Rosetti 1.0828 0.645 1.680 0.093 -0.181 2.346
Rosewill 2.894e-15 5.76e-15 0.503 0.615 -8.39e-15 1.42e-14
Rothschild 2.4403 0.481 5.077 0.000 1.498 3.382
Route 1.2113 0.411 2.949 0.003 0.406 2.016
Route 66 1.0959 0.644 1.700 0.089 -0.167 2.359
Roxy 1.8073 0.230 7.846 0.000 1.356 2.259
Royal 2.8636 0.655 4.375 0.000 1.581 4.147
Royal Robbins 2.3010 0.645 3.567 0.000 1.037 3.565
Ruby Rd. 1.7956 0.647 2.775 0.006 0.527 3.064
Ruby Rox 1.5846 0.480 3.302 0.001 0.644 2.525
Rue21 1.5311 0.221 6.917 0.000 1.097 1.965
Rusk 2.1615 0.647 3.339 0.001 0.893 3.430
Russ 1.961e-14 9.78e-15 2.005 0.045 4.36e-16 3.88e-14
Russell Athletic 1.7191 0.271 6.338 0.000 1.188 2.251
Rusty 1.3141 0.659 1.995 0.046 0.023 2.605
Ryka 1.5790 0.480 3.290 0.001 0.638 2.520
SEPHORA COLLECTION 1.7260 0.372 4.640 0.000 0.997 2.455
SKECHERS 1.6908 0.226 7.471 0.000 1.247 2.134
SKLZ 2.5024 0.712 3.516 0.000 1.107 3.898
SO 1.3555 0.258 5.245 0.000 0.849 1.862
SPANX 2.2324 0.254 8.786 0.000 1.734 2.730
SPY 1.7578 0.668 2.631 0.009 0.448 3.068
SUGOI 2.1507 0.646 3.332 0.001 0.885 3.416
Sabo Skirt -4.738e-15 6.27e-15 -0.756 0.450 -1.7e-14 7.55e-15
Safety st 1.1959 0.379 3.159 0.002 0.454 1.938
Sag Harbor 2.0067 0.717 2.799 0.005 0.602 3.412
Saga 1.329e-14 9.47e-15 1.404 0.160 -5.27e-15 3.19e-14
Saint Laurent 3.9129 0.271 14.424 0.000 3.381 4.445
Saks Fifth Avenue 1.9483 0.371 5.250 0.000 1.221 2.676
Sally Hansen 1.8428 0.242 7.603 0.000 1.368 2.318
Salomon 2.0709 0.480 4.314 0.000 1.130 3.012
Salt Lake Clothing 1.4794 0.480 3.083 0.002 0.539 2.420
Salvage 2.6818 0.644 4.161 0.000 1.419 3.945
Salvatore Ferragamo 2.9286 0.258 11.337 0.000 2.422 3.435
Sam & Libby 2.3605 0.645 3.660 0.000 1.096 3.625
Sam Edelman 2.4610 0.261 9.423 0.000 1.949 2.973
Samsonite 2.1864 0.647 3.379 0.001 0.918 3.454
Samsung 2.2660 0.215 10.525 0.000 1.844 2.688
Samsung Galaxy 2.6602 0.645 4.125 0.000 1.396 3.924
San Lorenzo 2.3770 0.313 7.587 0.000 1.763 2.991
SanDisk 1.5210 0.307 4.946 0.000 0.918 2.124
Sanctuary 2.1164 0.646 3.277 0.001 0.851 3.382
Sandra Darren 5.418e-15 9.03e-15 0.600 0.548 -1.23e-14 2.31e-14
Sandra Ingrish 2.1269 0.644 3.300 0.001 0.864 3.390
Sanrio -2.704e-15 7.63e-15 -0.354 0.723 -1.77e-14 1.23e-14
Sante 1.4560 0.644 2.260 0.024 0.193 2.719
Sanuk 1.9942 0.265 7.536 0.000 1.476 2.513
Sassy 1.8510 0.647 2.861 0.004 0.583 3.119
Saucony 1.7545 0.287 6.116 0.000 1.192 2.317
Sbicca -2.41e-15 7.09e-15 -0.340 0.734 -1.63e-14 1.15e-14
Scala -1.034e-14 7.58e-15 -1.364 0.173 -2.52e-14 4.52e-15
Scentsy 2.1427 0.222 9.646 0.000 1.707 2.578
Schick 2.0780 0.274 7.591 0.000 1.541 2.615
Schleich 1.7901 0.480 3.726 0.000 0.848 2.732
Scholastic 1.483e-14 7.56e-15 1.963 0.050 2.26e-17 2.96e-14
Scholastic Children 2.5619 0.647 3.958 0.000 1.293 3.831
Schutt 2.9683 0.658 4.513 0.000 1.679 4.257
Schwinn 1.8280 0.663 2.756 0.006 0.528 3.128
Scope Brand Shop 3.8381 0.646 5.945 0.000 2.573 5.104
Scosche 1.7043 0.647 2.635 0.008 0.437 2.972
Scotch & Soda 1.8967 0.646 2.934 0.003 0.630 3.164
Scott Paper 2.0585 0.394 5.224 0.000 1.286 2.831
Seagate 3.2202 0.654 4.923 0.000 1.938 4.502
Sean John 2.0138 0.413 4.875 0.000 1.204 2.823
Sears 1.5447 0.480 3.215 0.001 0.603 2.486
Sebago -3.956e-15 8.15e-15 -0.486 0.627 -1.99e-14 1.2e-14
Secret 2.0174 0.481 4.190 0.000 1.074 2.961
Sega 2.1948 0.411 5.344 0.000 1.390 3.000
Seiko 1.944e-14 8.71e-15 2.233 0.026 2.38e-15 3.65e-14
Select 4.0622 0.653 6.222 0.000 2.783 5.342
Self Esteem 0.8466 0.480 1.765 0.078 -0.094 1.787
SeneGence 2.8373 0.216 13.158 0.000 2.415 3.260
Sennheiser 2.0727 0.646 3.211 0.001 0.807 3.338
Sentry 1.8029 0.646 2.790 0.005 0.536 3.070
Sephora 2.1569 0.213 10.124 0.000 1.739 2.574
Sequin Hearts 6.253e-15 7.24e-15 0.863 0.388 -7.95e-15 2.05e-14
Sere Nade 1.8418 0.644 2.858 0.004 0.579 3.105
Serenity 1.4955 0.676 2.212 0.027 0.171 2.820
Seresto 3.1298 0.646 4.843 0.000 1.863 4.396
Sesame Street 1.8476 0.254 7.279 0.000 1.350 2.345
Seven7 2.2442 0.480 4.674 0.000 1.303 3.185
Seychelles 2.1120 0.645 3.277 0.001 0.849 3.375
Shabby Chic 2.0316 0.347 5.861 0.000 1.352 2.711
Sharagano 1.7609 0.645 2.732 0.006 0.498 3.024
Sharper Image 1.5083 0.484 3.116 0.002 0.559 2.457
Sharpie 1.7819 0.414 4.304 0.000 0.970 2.593
Shea Moisture 2.0320 0.507 4.008 0.000 1.038 3.026
Sheer Cover 1.1543 0.480 2.406 0.016 0.214 2.095
Shenanigans 1.8631 0.645 2.889 0.004 0.599 3.127
Sherpa -9.452e-15 6.68e-15 -1.415 0.157 -2.25e-14 3.64e-15
Sherri Hill 3.3719 0.274 12.290 0.000 2.834 3.910
Shimano Fishing 2.9287 0.654 4.477 0.000 1.647 4.211
Shiseido 2.3817 0.275 8.648 0.000 1.842 2.922
Shock Doctor 1.4906 0.645 2.310 0.021 0.226 2.755
Shoe Dazzle 1.7426 0.411 4.243 0.000 0.938 2.547
Show Me Your MuMu 2.9071 0.480 6.055 0.000 1.966 3.848
Signature 1.9365 0.645 3.001 0.003 0.672 3.201
Silence + Noise 2.0316 0.480 4.235 0.000 1.091 2.972
Silhouettes 1.8967 0.645 2.939 0.003 0.632 3.162
Silly Bandz 1.5186 0.645 2.354 0.019 0.254 2.783
Silpada 2.5152 0.302 8.325 0.000 1.923 3.107
Silver 2.3562 0.647 3.639 0.000 1.087 3.625
Silver Jeans 1.8797 0.481 3.906 0.000 0.937 2.823
Silver Jeans Co. 2.1711 0.233 9.323 0.000 1.715 2.628
Silverlit -5.71e-15 8.74e-15 -0.653 0.513 -2.28e-14 1.14e-14
Similac 1.9757 0.255 7.762 0.000 1.477 2.475
Simply Southern 2.1566 0.232 9.276 0.000 1.701 2.612
Simply Vera Vera Wang 1.6530 0.276 6.000 0.000 1.113 2.193
Sinful 2.903e-15 7.01e-15 0.414 0.679 -1.08e-14 1.66e-14
Sinful By Affliction 1.8663 0.275 6.775 0.000 1.326 2.406
Singing Machine -8.257e-15 5.65e-15 -1.462 0.144 -1.93e-14 2.81e-15
Sirius Satellite Radio 1.9626 0.511 3.840 0.000 0.961 2.964
Skagen 2.0493 0.645 3.177 0.001 0.785 3.314
Sketchers 1.6890 0.371 4.552 0.000 0.962 2.416
Skin Industries 1.5554 0.645 2.413 0.016 0.292 2.819
Skip Hop 1.6939 0.273 6.211 0.000 1.159 2.229
Skullcandy 3.2769 0.486 6.737 0.000 2.324 4.230
Sky 1.7891 0.645 2.775 0.006 0.525 3.053
Slatkin & Co. 1.0626 0.646 1.645 0.100 -0.203 2.328
Sleek MakeUP 1.6472 0.410 4.013 0.000 0.843 2.452
Slime® 1.9144 0.645 2.968 0.003 0.650 3.179
SmartWool 2.2281 0.373 5.966 0.000 1.496 2.960
Smartzone -2.05e-14 1.24e-14 -1.653 0.098 -4.48e-14 3.8e-15
Smashbox 2.1086 0.222 9.505 0.000 1.674 2.543
Snap 2.6052 0.644 4.042 0.000 1.342 3.868
Snap-on 6.44e-15 6.67e-15 0.966 0.334 -6.62e-15 1.95e-14
Snuggie 1.6464 0.373 4.414 0.000 0.915 2.378
Sock It to Me -1.515e-15 7.39e-15 -0.205 0.838 -1.6e-14 1.3e-14
Soda 1.9702 0.411 4.798 0.000 1.165 2.775
Soffe 1.5897 0.302 5.257 0.000 0.997 2.182
Soft Surroundings 1.5463 0.480 3.221 0.001 0.605 2.487
Softcup 1.485e-14 8.4e-15 1.769 0.077 -1.6e-15 3.13e-14
Sole Society 1.7755 0.645 2.755 0.006 0.512 3.039
Solo 2.4555 0.647 3.795 0.000 1.187 3.724
Soma 2.3633 0.410 5.758 0.000 1.559 3.168
Sonia Kashuk 2.9021 0.645 4.497 0.000 1.637 4.167
Sonoma 1.4633 0.257 5.699 0.000 0.960 1.966
Sony 2.1953 0.215 10.221 0.000 1.774 2.616
Sony Pictures 1.6717 0.411 4.071 0.000 0.867 2.477
Soprano 1.5614 0.645 2.423 0.015 0.298 2.825
South Pole 6.475e-15 9.29e-15 0.697 0.486 -1.17e-14 2.47e-14
Southern Living 2.2370 0.480 4.660 0.000 1.296 3.178
Southern Marsh 2.3072 0.314 7.355 0.000 1.692 2.922
Southpole 1.8424 0.294 6.267 0.000 1.266 2.419
Spalding 2.5624 0.366 6.992 0.000 1.844 3.281
Speck 2.2774 0.644 3.534 0.000 1.014 3.540
Spectra 5.969e-15 7.6e-15 0.785 0.432 -8.93e-15 2.09e-14
Speechless 1.8157 0.327 5.557 0.000 1.175 2.456
Speedo 1.7360 0.288 6.019 0.000 1.171 2.301
Spell & The Gypsy Collective 4.0396 0.371 10.889 0.000 3.312 4.767
Spencer's 3.7785 0.649 5.822 0.000 2.507 5.051
Spense 1.1602 0.481 2.412 0.016 0.217 2.103
Spenser Jeremy 0.8610 0.644 1.336 0.182 -0.402 2.124
Sperry 2.2031 0.242 9.109 0.000 1.729 2.677
Sperry Top-Sider 2.1478 0.304 7.074 0.000 1.553 2.743
Sperrys 2.2373 0.246 9.087 0.000 1.755 2.720
Spider-Man 1.9442 0.645 3.014 0.003 0.680 3.209
Spiegel 1.8901 0.645 2.932 0.003 0.626 3.154
Spigen -1.773e-14 8.07e-15 -2.198 0.028 -3.35e-14 -1.92e-15
Spin Master 3.0450 0.248 12.277 0.000 2.559 3.531
Spitfire -1.414e-14 7.64e-15 -1.851 0.064 -2.91e-14 8.35e-16
Splendid 1.8033 0.480 3.757 0.000 0.862 2.744
Sport Savvy 1.2502 0.644 1.940 0.052 -0.013 2.513
Spy Tec 2.2467 0.645 3.481 0.000 0.982 3.512
Spyder 2.4309 0.284 8.552 0.000 1.874 2.988
St. Ives 2.0246 0.481 4.205 0.000 1.081 2.968
St. John 2.2800 0.648 3.521 0.000 1.011 3.549
St. John's Bay 1.3976 0.314 4.448 0.000 0.782 2.013
Stafford 1.9871 0.653 3.041 0.002 0.706 3.268
Stamped 2.8499 0.223 12.803 0.000 2.414 3.286
Stance 2.4639 0.315 7.826 0.000 1.847 3.081
Stanley 2.0936 0.645 3.246 0.001 0.830 3.358
Star City 1.2558 0.647 1.940 0.052 -0.013 2.525
Star Wars 2.0103 0.226 8.876 0.000 1.566 2.454
Starbucks 1.9441 0.228 8.523 0.000 1.497 2.391
Staring at Stars 1.8014 0.644 2.795 0.005 0.538 3.065
Starmark 1.1098 0.646 1.717 0.086 -0.157 2.376
Starter 1.8544 0.289 6.424 0.000 1.289 2.420
Stats 2.1488 0.657 3.268 0.001 0.860 3.437
Stayfree 1.634e-15 6.22e-15 0.263 0.793 -1.06e-14 1.38e-14
Stella & Dot 2.6228 0.256 10.241 0.000 2.121 3.125
Stella McCartney 3.2702 0.645 5.074 0.000 2.007 4.534
Sterling Industries 2.0222 0.664 3.045 0.002 0.721 3.324
Stetson 2.4022 0.645 3.726 0.000 1.138 3.666
Steve & Barry's 1.2953 0.480 2.699 0.007 0.355 2.236
Steve Madden 2.0051 0.218 9.197 0.000 1.578 2.432
Steven by Steve Madden 3.7913 0.645 5.882 0.000 2.528 5.055
Stila 2.3079 0.225 10.248 0.000 1.866 2.749
Stitches 1.2069 0.645 1.870 0.061 -0.058 2.472
Stokke 1.5489 0.679 2.281 0.023 0.218 2.880
Stone Fox Swim 3.6736 0.480 7.652 0.000 2.733 4.615
Stoosh 1.7633 0.644 2.736 0.006 0.500 3.026
Stranded 1.4081 0.480 2.933 0.003 0.467 2.349
Stride Rite 1.7412 0.239 7.287 0.000 1.273 2.210
Stuart Weitzman 3.6299 0.294 12.356 0.000 3.054 4.206
Studio Ease 1.5103 0.648 2.329 0.020 0.239 2.781
Studio I 1.4296 0.645 2.218 0.027 0.166 2.693
Studio Works 1.626e-14 9.91e-15 1.640 0.101 -3.17e-15 3.57e-14
Studio Y 1.2365 0.646 1.914 0.056 -0.030 2.503
Stussy 2.1021 0.261 8.050 0.000 1.590 2.614
Style & Co 1.7524 0.372 4.710 0.000 1.023 2.482
Style&co. 1.8919 0.294 6.443 0.000 1.316 2.467
Stüssy 1.9303 0.345 5.594 0.000 1.254 2.607
Suave 1.9799 0.348 5.697 0.000 1.299 2.661
Sugarpill 2.6058 0.371 7.025 0.000 1.879 3.333
Suit Studio 1.2979 0.645 2.014 0.044 0.035 2.561
Summer Infant 1.7071 0.316 5.397 0.000 1.087 2.327
Summer's Eve 1.7438 0.646 2.701 0.007 0.478 3.009
Sunbeam 1.677e-14 8.14e-15 2.060 0.039 8.18e-16 3.27e-14
Sundance 1.0828 0.645 1.680 0.093 -0.181 2.346
Sunny Leigh 1.7296 0.644 2.684 0.007 0.466 2.993
Superdry 2.4402 0.481 5.074 0.000 1.498 3.383
Supra 2.1763 0.645 3.375 0.001 0.912 3.440
Supreme 2.8198 0.223 12.658 0.000 2.383 3.256
Susan Bristol -1.32e-14 1.13e-14 -1.166 0.244 -3.54e-14 9e-15
Susan Graver 1.9137 0.411 4.659 0.000 1.109 2.719
Sutton Studio -6.34e-15 8.15e-15 -0.778 0.437 -2.23e-14 9.64e-15
Swarovski 2.5982 0.268 9.710 0.000 2.074 3.123
Sweet Pea 1.8988 0.645 2.943 0.003 0.634 3.163
Swell 1.8571 0.645 2.880 0.004 0.593 3.121
Swiffer 1.1163 0.775 1.441 0.150 -0.402 2.635
Swiss Legend 1.6316 0.645 2.529 0.011 0.367 2.896
Sylvania 1.9249 0.666 2.889 0.004 0.619 3.231
T by Alexander Wang 6.154e-15 8.37e-15 0.735 0.462 -1.03e-14 2.26e-14
T-fal 1.8798 0.654 2.875 0.004 0.598 3.161
T.J.Maxx 6.913e-15 8.08e-15 0.855 0.393 -8.93e-15 2.28e-14
TENA -1.131e-14 8.78e-15 -1.289 0.197 -2.85e-14 5.89e-15
TOMS 2.0987 0.224 9.365 0.000 1.660 2.538
TOMY -3.488e-15 1.07e-14 -0.327 0.744 -2.44e-14 1.74e-14
TRESemme 2.8952 0.646 4.484 0.000 1.630 4.161
TYR 1.6460 0.645 2.550 0.011 0.381 2.911
Taboo 1.5170 0.480 3.162 0.002 0.577 2.457
Tadashi Shoji 3.5274 0.645 5.468 0.000 2.263 4.792
Tag 1.9045 0.645 2.955 0.003 0.641 3.168
Tag Heuer 3.6857 0.645 5.713 0.000 2.421 4.950
Tahari 1.9076 0.379 5.028 0.000 1.164 2.651
Tail 1.8780 0.480 3.911 0.000 0.937 2.819
Takeout -9.505e-15 6.9e-15 -1.378 0.168 -2.3e-14 4.01e-15
Talbots 2.0210 0.287 7.045 0.000 1.459 2.583
Tampax 2.0719 0.373 5.552 0.000 1.340 2.803
Tangerine 1.4054 0.480 2.930 0.003 0.465 2.346
Tangle Teezer 1.6092 0.646 2.493 0.013 0.344 2.874
TapouT 2.473e-15 7.97e-15 0.310 0.756 -1.32e-14 1.81e-14
Target 1.7390 0.220 7.918 0.000 1.309 2.169
Targus 1.6877 0.374 4.516 0.000 0.955 2.420
Tarte 2.3008 0.215 10.725 0.000 1.880 2.721
Tassimo 2.1290 0.481 4.431 0.000 1.187 3.071
Tatcha 2.2738 0.644 3.528 0.000 1.011 3.537
Taylor 1.7680 0.482 3.666 0.000 0.823 2.713
TaylorMade 2.6563 0.419 6.338 0.000 1.835 3.478
Ted Baker 3.4583 0.645 5.365 0.000 2.195 4.722
Ted Baker London 3.2392 0.294 11.021 0.000 2.663 3.815
TeeFury 1.5811 0.480 3.295 0.001 0.641 2.522
Teenage Mutant Ninja Turtles 1.5637 0.412 3.795 0.000 0.756 2.371
Tek Gear 1.5557 0.327 4.761 0.000 0.915 2.196
Tek Nek 2.651e-14 8.49e-15 3.121 0.002 9.86e-15 4.32e-14
Teleflora 1.094e-14 8.61e-15 1.271 0.204 -5.93e-15 2.78e-14
Tennessee River 2.396e-14 9.26e-15 2.587 0.010 5.81e-15 4.21e-14
Terani Couture -2.783e-15 7.24e-15 -0.384 0.701 -1.7e-14 1.14e-14
Tetra 1.2120 0.648 1.871 0.061 -0.058 2.482
Teva 1.6767 0.480 3.493 0.000 0.736 2.618
Texas Instruments 2.6018 0.413 6.296 0.000 1.792 3.412
The Body Shop 1.9594 0.302 6.486 0.000 1.367 2.551
The Children's Place 1.7817 0.225 7.904 0.000 1.340 2.224
The First Years 1.8883 0.418 4.520 0.000 1.070 2.707
The Hundreds 2.0436 0.294 6.957 0.000 1.468 2.619
The Instrument Store 2.6115 0.518 5.040 0.000 1.596 3.627
The Limited 1.6716 0.255 6.566 0.000 1.173 2.171
The Maya Group 2.8795 0.645 4.464 0.000 1.615 4.144
The Mountain 1.5358 0.645 2.383 0.017 0.272 2.799
The North Face 2.3649 0.215 11.013 0.000 1.944 2.786
The Northwest Company 1.5841 0.647 2.449 0.014 0.316 2.852
The Sak 1.7979 0.345 5.206 0.000 1.121 2.475
Theory 5.504e-15 8.77e-15 0.627 0.530 -1.17e-14 2.27e-14
Thermos 2.7812 0.651 4.270 0.000 1.505 4.058
Thinkway 1.3112 0.645 2.033 0.042 0.047 2.575
Thomas & Friends 1.8869 0.246 7.658 0.000 1.404 2.370
Thor 0.9948 0.690 1.441 0.150 -0.358 2.348
Thrasher 2.1844 0.313 6.976 0.000 1.571 2.798
Thrasher Magazine 2.1012 0.268 7.853 0.000 1.577 2.626
Threadless 1.9433 0.644 3.016 0.003 0.680 3.206
Three Dots 0.9456 0.645 1.467 0.142 -0.318 2.209
Tiana b 1.193e-14 8.31e-15 1.436 0.151 -4.36e-15 2.82e-14
Tianello 2.7063 0.645 4.199 0.000 1.443 3.970
Tick Twister 1.347e-14 5.35e-15 2.519 0.012 2.99e-15 2.39e-14
Tide 1.6658 0.483 3.449 0.001 0.719 2.612
Tieks 3.4673 0.411 8.438 0.000 2.662 4.273
Tiffany & Co. 3.8153 0.222 17.159 0.000 3.380 4.251
Tiger 2.2024 0.414 5.319 0.000 1.391 3.014
Tignanello -1.088e-14 7.48e-15 -1.454 0.146 -2.55e-14 3.79e-15
Tile 1.5805 0.679 2.326 0.020 0.249 2.912
Tilly's 1.6009 0.345 4.636 0.000 0.924 2.278
Timberland 2.5126 0.226 11.120 0.000 2.070 2.956
Timing 1.268e-14 9.78e-15 1.297 0.195 -6.48e-15 3.18e-14
Tinker Bell -1.47e-14 1.11e-14 -1.321 0.187 -3.65e-14 7.12e-15
Tinseltown 1.7928 0.483 3.710 0.000 0.846 2.740
Tiny Love 2.2235 0.487 4.569 0.000 1.270 3.177
Titleist 2.3886 0.374 6.392 0.000 1.656 3.121
Toastmaster 1.6250 0.416 3.904 0.000 0.809 2.441
Tobi 2.0397 0.237 8.624 0.000 1.576 2.503
Tod's 3.9779 0.645 6.167 0.000 2.714 5.242
Tolly Tots 1.763e-14 8.33e-15 2.117 0.034 1.31e-15 3.39e-14
Tom Ford 2.8138 0.302 9.308 0.000 2.221 3.406
Tommee Tippee 2.0141 0.260 7.747 0.000 1.504 2.524
Tommy Bahama 2.1818 0.313 6.966 0.000 1.568 2.796
Tommy Hilfiger 2.0423 0.218 9.383 0.000 1.616 2.469
Tonka 1.6401 0.486 3.376 0.001 0.688 2.592
Tony & Tina 1.3393 0.644 2.078 0.038 0.076 2.602
Tony Hawk 1.1552 0.480 2.406 0.016 0.214 2.096
Tony Lama 3.1505 0.480 6.561 0.000 2.209 4.092
Tony Moly 2.1339 0.645 3.311 0.001 0.871 3.397
Too Faced 2.2715 0.214 10.600 0.000 1.851 2.691
Tootsie 2.2397 0.657 3.411 0.001 0.953 3.527
Top Fin -6.893e-16 1.06e-14 -0.065 0.948 -2.15e-14 2.01e-14
Top Paw 1.7799 0.347 5.135 0.000 1.101 2.459
TopShop 2.1572 0.240 8.973 0.000 1.686 2.628
Topman 1.7125 0.480 3.569 0.000 0.772 2.653
Topps 2.0512 0.246 8.323 0.000 1.568 2.534
Torrid 2.0884 0.217 9.624 0.000 1.663 2.514
Tory Burch 3.2241 0.216 14.928 0.000 2.801 3.647
Toshiba 2.5650 0.330 7.780 0.000 1.919 3.211
Toy Monster 0.8895 0.645 1.379 0.168 -0.375 2.154
Toys R Us 1.8426 0.346 5.326 0.000 1.164 2.521
Tracy Evans 2.2252 0.648 3.437 0.001 0.956 3.494
Travis Mathew 2.5336 0.482 5.252 0.000 1.588 3.479
Treasure & Bond 2.1156 0.649 3.258 0.001 0.843 3.388
Triangl 2.8751 0.246 11.664 0.000 2.392 3.358
Trina Turk 2.3902 0.481 4.969 0.000 1.447 3.333
Triple Five Soul 2.327e-15 8.87e-15 0.262 0.793 -1.51e-14 1.97e-14
Tripp NYC 2.2147 0.372 5.949 0.000 1.485 2.944
Trish McEvoy 1.6577 0.410 4.040 0.000 0.854 2.462
Trixxi 5.584e-15 6.18e-15 0.903 0.367 -6.54e-15 1.77e-14
True Religion 2.4091 0.327 7.373 0.000 1.769 3.049
True Religion Brand Jeans 2.5673 0.219 11.700 0.000 2.137 2.997
Tulle 2.0644 0.668 3.090 0.002 0.755 3.374
Tultex 1.9950 0.411 4.856 0.000 1.190 2.800
Tumi 2.9200 0.346 8.441 0.000 2.242 3.598
Tupperware 2.0684 0.245 8.438 0.000 1.588 2.549
Turtle Beach 2.2196 0.481 4.617 0.000 1.277 3.162
Twenty One 1.7806 0.327 5.448 0.000 1.140 2.421
Twiggy 2.0784 0.645 3.224 0.001 0.815 3.342
Twilight Gypsy Collective 0.7295 0.645 1.132 0.258 -0.534 1.993
Twisted Heart -3.383e-15 8.63e-15 -0.392 0.695 -2.03e-14 1.35e-14
Twisted X -7.203e-15 1.02e-14 -0.706 0.480 -2.72e-14 1.28e-14
UFC 1.6993 0.464 3.662 0.000 0.790 2.609
UGG 1.8031 0.346 5.216 0.000 1.126 2.481
UGG Australia 2.6868 0.215 12.491 0.000 2.265 3.108
UNIF 2.6202 0.251 10.445 0.000 2.128 3.112
UNIONBAY 1.7822 0.328 5.439 0.000 1.140 2.424
UPPAbaby 5.17e-15 7.59e-15 0.681 0.496 -9.71e-15 2e-14
US POLO ASSN 1.7721 0.264 6.707 0.000 1.254 2.290
Ubisoft 2.1342 0.645 3.310 0.001 0.871 3.398
Ulta 1.8713 0.221 8.481 0.000 1.439 2.304
Ulta Salon, Cosmetics & Fragrance, Inc. 1.8033 0.645 2.797 0.005 0.540 3.067
Ultimate Ears 3.9003 0.647 6.031 0.000 2.633 5.168
UltimateTV RCA 1.9076 0.375 5.089 0.000 1.173 2.642
Ultra Flirt 0.9529 0.644 1.479 0.139 -0.310 2.216
Umbra 2.2634 0.486 4.656 0.000 1.310 3.216
Umbro 1.9989 0.654 3.059 0.002 0.718 3.280
Undefeated 2.0127 0.480 4.194 0.000 1.072 2.953
Under Armour 1.9537 0.214 9.142 0.000 1.535 2.373
Uniden 2.8436 0.647 4.397 0.000 1.576 4.111
Uniqlo 1.6531 0.327 5.057 0.000 1.012 2.294
Universal Baby 1.0962 0.647 1.695 0.090 -0.171 2.363
Universal Studios 1.9951 0.327 6.102 0.000 1.354 2.636
Univsersal Studios 1.4889 0.646 2.303 0.021 0.222 2.756
Unknown 1.9891 0.212 9.383 0.000 1.574 2.405
Unlisted -1.191e-14 8.63e-15 -1.381 0.167 -2.88e-14 5e-15
Urban Behavior 1.9179 0.481 3.986 0.000 0.975 2.861
Urban Decay 2.2649 0.214 10.573 0.000 1.845 2.685
Urban Outfitters 1.9420 0.218 8.893 0.000 1.514 2.370
Urban Pipeline 1.8547 0.347 5.342 0.000 1.174 2.535
VANS 1.8575 0.216 8.615 0.000 1.435 2.280
VIGOSS 2.0610 0.276 7.455 0.000 1.519 2.603
VTech 1.9261 0.259 7.429 0.000 1.418 2.434
Va Va Voom -4.254e-15 8.86e-15 -0.480 0.631 -2.16e-14 1.31e-14
Valentino 3.3937 0.302 11.228 0.000 2.801 3.986
Valerie Stevens 1.9436 0.648 3.001 0.003 0.674 3.213
Van Heusen 1.7564 0.413 4.254 0.000 0.947 2.566
Vanilla Star 1.1128 0.645 1.726 0.084 -0.151 2.376
Vanity 1.6117 0.256 6.294 0.000 1.110 2.114
Vaseline 1.4281 0.481 2.966 0.003 0.484 2.372
Velvet Torch 2.2398 0.644 3.475 0.001 0.977 3.503
Venezia 1.7952 0.411 4.371 0.000 0.990 2.600
Venus 1.7027 0.287 5.942 0.000 1.141 2.264
Vera Bradley 1.9994 0.216 9.274 0.000 1.577 2.422
Vera Wang 2.1383 0.327 6.544 0.000 1.498 2.779
Versace 2.5225 0.251 10.068 0.000 2.031 3.014
Versace Collection 3.1782 0.645 4.927 0.000 1.914 4.442
Vertigo 4.0413 0.645 6.268 0.000 2.778 5.305
Via Spiga 2.0976 0.411 5.109 0.000 1.293 2.902
Vibram 1.7897 0.480 3.729 0.000 0.849 2.730
Vichy 1.9469 0.411 4.741 0.000 1.142 2.752
Vicks 1.6706 0.527 3.172 0.002 0.638 2.703
Victor Costa 2.2211 0.645 3.445 0.001 0.957 3.485
Victoria Beckham 2.2867 0.480 4.766 0.000 1.346 3.227
Victoria's Secret 2.1646 0.212 10.197 0.000 1.749 2.581
Victorinox Swiss Army® -4.657e-15 9.23e-15 -0.505 0.614 -2.27e-14 1.34e-14
ViewSonic 2.6710 0.645 4.143 0.000 1.407 3.935
Viktor & Rolf 0.8249 0.645 1.279 0.201 -0.439 2.089
Vince 2.5292 0.480 5.272 0.000 1.589 3.469
Vince Camuto 2.2746 0.240 9.493 0.000 1.805 2.744
Vincent Longo 1.8197 0.480 3.794 0.000 0.880 2.760
Vintage 2.2059 0.218 10.112 0.000 1.778 2.633
Violet & Claire 1.3483 0.480 2.809 0.005 0.408 2.289
Virgin Only 3.6156 0.314 11.500 0.000 2.999 4.232
Vitamin A 2.1914 0.645 3.400 0.001 0.928 3.455
Vitamix 2.7221 0.657 4.142 0.000 1.434 4.010
Vocal 1.8939 0.480 3.948 0.000 0.954 2.834
Volcom 1.8886 0.244 7.744 0.000 1.411 2.367
Von Dutch 2.1848 0.654 3.339 0.001 0.902 3.467
WILLOW 2.3814 0.483 4.927 0.000 1.434 3.329
WOW couture -7.757e-15 8e-15 -0.969 0.332 -2.34e-14 7.93e-15
WWE 2.2515 0.234 9.637 0.000 1.794 2.709
Wacom 2.4041 0.483 4.976 0.000 1.457 3.351
Wagner Lighting 2.8574 0.653 4.376 0.000 1.578 4.137
Wahl 2.7960 0.645 4.332 0.000 1.531 4.061
Walls 2.4722 0.655 3.775 0.000 1.189 3.756
Waring -1.584e-14 8.92e-15 -1.777 0.076 -3.33e-14 1.64e-15
Warner Bros. 1.7259 0.647 2.667 0.008 0.458 2.994
Warner Brothers 0.8216 0.655 1.254 0.210 -0.463 2.106
Warrior 3.3822 0.482 7.010 0.000 2.437 4.328
Waterford 1.8804 0.645 2.917 0.004 0.617 3.144
Waterpik 2.5252 0.412 6.122 0.000 1.717 3.334
Waverly 1.6265 0.650 2.502 0.012 0.352 2.901
We The Free 7.951e-15 7.15e-15 1.112 0.266 -6.07e-15 2.2e-14
Weatherproof 2.01e-14 7.34e-15 2.739 0.006 5.72e-15 3.45e-14
Weaver Leather 1.2921 0.646 1.999 0.046 0.025 2.559
Weavers 0.9529 0.644 1.479 0.139 -0.310 2.216
Weight Watchers 1.905e-14 7.63e-15 2.497 0.013 4.1e-15 3.4e-14
West Elm -3.638e-15 9.47e-15 -0.384 0.701 -2.22e-14 1.49e-14
Western Digital 2.9282 0.363 8.064 0.000 2.216 3.640
Wet Seal 1.5936 0.222 7.170 0.000 1.158 2.029
Wet n Wild 1.5353 0.221 6.947 0.000 1.102 1.968
Whisker City 1.4445 0.484 2.982 0.003 0.495 2.394
White House Black Market 1.9851 0.233 8.519 0.000 1.528 2.442
White Stag 1.5190 0.287 5.300 0.000 0.957 2.081
Wigwam 1.9997 0.645 3.099 0.002 0.735 3.264
Wild Diva 1.6561 0.411 4.032 0.000 0.851 2.461
Wildfox 2.8622 0.345 8.288 0.000 2.185 3.539
Wildfox Couture 2.6723 0.250 10.669 0.000 2.181 3.163
Wildkin 1.317e-14 7.02e-15 1.875 0.061 -6e-16 2.69e-14
Willi Smith 1.3962 0.480 2.910 0.004 0.456 2.337
William Rast 1.8313 0.645 2.841 0.004 0.568 3.095
Williams Sonoma 1.6945 0.413 4.106 0.000 0.886 2.503
Willow Tree 2.2132 0.480 4.611 0.000 1.272 3.154
Wilson 2.0604 0.323 6.369 0.000 1.426 2.694
Wilsons Leather 2.1720 0.314 6.926 0.000 1.557 2.787
Wilton 1.6151 0.290 5.563 0.000 1.046 2.184
Windsor 1.9616 0.236 8.319 0.000 1.499 2.424
Wish 1.2282 0.644 1.906 0.057 -0.035 2.491
Wit & Wisdom 1.109e-14 8.34e-15 1.329 0.184 -5.26e-15 2.74e-14
Wizards of the Coast 2.1765 0.257 8.460 0.000 1.672 2.681
Wolfgang Puck 1.9821 0.656 3.023 0.003 0.697 3.267
Wolverine 2.0720 0.645 3.214 0.001 0.808 3.336
Woman Within -1.873e-14 8.95e-15 -2.093 0.036 -3.63e-14 -1.19e-15
Woolrich 1.7127 0.415 4.129 0.000 0.900 2.526
Worthington 1.5959 0.296 5.398 0.000 1.016 2.175
WowWee 1.8748 0.413 4.544 0.000 1.066 2.683
Wrangler 1.8614 0.273 6.821 0.000 1.327 2.396
WubbaNub 2.4671 0.420 5.869 0.000 1.643 3.291
XEN-TAN 1.781e-14 6.61e-15 2.696 0.007 4.86e-15 3.08e-14
XM 1.9407 0.647 3.001 0.003 0.673 3.208
XOXO 1.4731 0.294 5.017 0.000 0.898 2.049
XXI 1.5066 0.327 4.610 0.000 0.866 2.147
Xbox 2.1221 0.215 9.859 0.000 1.700 2.544
Xersion 1.3745 0.314 4.382 0.000 0.760 1.989
Xhilaration 1.5559 0.220 7.076 0.000 1.125 1.987
Xscape 2.1210 0.412 5.153 0.000 1.314 2.928
YMI 1.6849 0.268 6.291 0.000 1.160 2.210
YMI Jeans 2.0989 0.371 5.655 0.000 1.371 2.826
YSL Yves Saint Laurent 2.7949 0.411 6.798 0.000 1.989 3.601
Ya Los Angeles 1.052e-15 7.09e-15 0.148 0.882 -1.28e-14 1.49e-14
Yankee Candle 1.7896 0.238 7.529 0.000 1.324 2.255
Yeezy 3.3173 0.265 12.531 0.000 2.798 3.836
Yellow Box 1.1469 0.645 1.779 0.075 -0.116 2.410
Yeti 2.4248 0.244 9.941 0.000 1.947 2.903
Yoana Baraschi 2.4487 0.645 3.799 0.000 1.185 3.712
Yoki 1.2230 0.645 1.896 0.058 -0.041 2.487
Yosi Samra 2.9026 0.481 6.039 0.000 1.961 3.845
Young & Reckless 1.9540 0.313 6.240 0.000 1.340 2.568
Young Fabulous & Broke 2.6018 0.644 4.037 0.000 1.339 3.865
Younique 2.4400 0.220 11.099 0.000 2.009 2.871
Yummie Tummie -5.637e-15 6.31e-15 -0.894 0.371 -1.8e-14 6.72e-15
Yummie by Heather Thomson -9.704e-15 8.89e-15 -1.092 0.275 -2.71e-14 7.72e-15
Yves Saint Laurent 2.4582 0.235 10.453 0.000 1.997 2.919
ZARA 2.1243 0.219 9.686 0.000 1.694 2.554
ZENA 1.4919 0.645 2.315 0.021 0.229 2.755
Zac Posen 2.4081 0.644 3.737 0.000 1.145 3.671
Zales -1.114e-14 6.43e-15 -1.732 0.083 -2.37e-14 1.47e-15
Zana Di 1.1712 0.480 2.440 0.015 0.230 2.112
Zco. 1.45e-14 8.97e-15 1.617 0.106 -3.08e-15 3.21e-14
Zella 1.9189 0.286 6.698 0.000 1.357 2.480
Zen Group 8.286e-15 8.48e-15 0.977 0.329 -8.34e-15 2.49e-14
Zenana Outfitters 0.9451 0.480 1.970 0.049 0.005 1.885
ZeroXposur 2.1427 0.480 4.465 0.000 1.202 3.083
Zion Rootswear 1.9050 0.411 4.639 0.000 1.100 2.710
Zobo 5.842e-15 7.03e-15 0.831 0.406 -7.94e-15 1.96e-14
Zojirushi 2.6581 0.653 4.073 0.000 1.379 3.937
Zoo Med 1.6995 0.416 4.089 0.000 0.885 2.514
Zoo York 1.4379 0.481 2.991 0.003 0.496 2.380
Zoot 1.4024 0.644 2.176 0.030 0.139 2.665
Zum Zum 1.7666 0.645 2.738 0.006 0.502 3.031
Zumba 1.9812 0.281 7.058 0.000 1.431 2.531
a'gaci 1.8077 0.302 5.980 0.000 1.215 2.400
a.n.a 1.7462 0.276 6.337 0.000 1.206 2.286
aden & anais 2.0680 0.251 8.224 0.000 1.575 2.561
adidas NEO 2.7065 0.645 4.194 0.000 1.442 3.971
adidas Originals 2.6929 0.230 11.701 0.000 2.242 3.144
adidas by Stella McCartney 2.3786 0.645 3.690 0.000 1.115 3.642
aerie 1.5374 0.261 5.888 0.000 1.026 2.049
b.o.c. -2.124e-17 5.43e-15 -0.004 0.997 -1.07e-14 1.06e-14
bareMinerals 2.1262 0.231 9.216 0.000 1.674 2.578
beautyblender® 2.2947 0.253 9.066 0.000 1.799 2.791
bernie mev. 1.7661 0.645 2.737 0.006 0.501 3.031
bp 2.2763 0.644 3.532 0.000 1.013 3.539
bumGenius 2.4665 0.309 7.975 0.000 1.860 3.073
dELiA*s 1.8856 0.294 6.418 0.000 1.310 2.461
deborah lippmann 2.4326 0.645 3.772 0.000 1.168 3.697
dreambaby 1.6653 0.652 2.554 0.011 0.387 2.943
dressbarn 1.6836 0.271 6.207 0.000 1.152 2.215
e.l.f. 1.4447 0.223 6.470 0.000 1.007 1.882
elite HOCKEY 1.8891 0.663 2.851 0.004 0.591 3.188
eos 1.5922 0.281 5.665 0.000 1.041 2.143
ethika 2.0385 0.645 3.162 0.002 0.775 3.302
gDiapers -1.884e-17 6.42e-15 -0.003 0.998 -1.26e-14 1.26e-14
grass 1.4419 0.644 2.237 0.025 0.179 2.705
iXCC 1.6712 0.644 2.593 0.010 0.408 2.934
kate spade new york 2.8704 0.240 11.975 0.000 2.401 3.340
kathy ireland 1.3110 0.410 3.194 0.001 0.506 2.115
l.e.i. 1.5061 0.276 5.464 0.000 0.966 2.046
lei 4.611e-16 6.89e-15 0.067 0.947 -1.3e-14 1.4e-14
lillebaby 3.5157 0.647 5.437 0.000 2.248 4.783
lululemon athletica 2.8431 0.222 12.783 0.000 2.407 3.279
mamaRoo 3.9304 0.690 5.695 0.000 2.578 5.283
mandee 1.7379 0.480 3.622 0.000 0.798 2.678
me too 1.521e-14 8.63e-15 1.762 0.078 -1.71e-15 3.21e-14
metrostyle 1.9158 0.717 2.672 0.008 0.511 3.321
monster high 1.4710 0.645 2.280 0.023 0.206 2.736
patagonia 2.6973 0.219 12.316 0.000 2.268 3.127
pet brands 1.9306 0.374 5.159 0.000 1.197 2.664
prAna 2.0732 0.345 6.006 0.000 1.397 2.750
rag & bone 2.6418 0.287 9.207 0.000 2.079 3.204
rue 1.4831 0.227 6.541 0.000 1.039 1.927
theBalm 1.9589 0.261 7.504 0.000 1.447 2.471
timi & leslie 1.155e-14 8.95e-15 1.290 0.197 -6e-15 2.91e-14
tokidoki 2.1099 0.254 8.302 0.000 1.612 2.608
totes ISOTONER 1.7602 0.646 2.724 0.006 0.494 3.027
vineyard vines 2.2357 0.220 10.169 0.000 1.805 2.667
Antique 0.7647 0.175 4.365 0.000 0.421 1.108
Apparel 0.3071 0.200 1.533 0.125 -0.086 0.700
Art 0.0798 0.184 0.433 0.665 -0.281 0.441
Artwork 0.6238 0.307 2.029 0.042 0.021 1.226
Athletic Apparel 0.5158 0.158 3.272 0.001 0.207 0.825
Automotive 0.4337 0.210 2.069 0.039 0.023 0.845
Bags and Purses -0.0665 0.481 -0.138 0.890 -1.009 0.876
Bath 0.6240 0.462 1.352 0.177 -0.281 1.529
Bath & Body 0.7462 0.197 3.785 0.000 0.360 1.133
Bathing & Skin Care -0.4168 0.484 -0.862 0.389 -1.365 0.531
Bedding 0.5909 0.631 0.936 0.349 -0.646 1.828
Blazers & Sport Coats 0.4948 0.852 0.581 0.561 -1.175 2.165
Book 0.5544 0.295 1.878 0.060 -0.024 1.133
Books 0.3807 0.183 2.085 0.037 0.023 0.739
Books and Zines 0.6941 0.488 1.424 0.155 -0.262 1.650
Boys (4+) 0.5952 0.152 3.921 0.000 0.298 0.893
Boys 0-24 Mos 0.2028 0.151 1.341 0.180 -0.094 0.499
Boys 2T-5T 0.2435 0.151 1.610 0.107 -0.053 0.540
Cameras & Photography -0.7944 0.638 -1.244 0.213 -2.046 0.457
Candles 0.6936 0.275 2.523 0.012 0.155 1.233
Car Audio, Video & GPS 0.4553 0.247 1.843 0.065 -0.029 0.939
Car Seats & Accessories 0.3676 0.181 2.029 0.042 0.013 0.723
Cell Phones & Accessories 0.2668 0.268 0.996 0.319 -0.258 0.792
Ceramics and Pottery 0.5475 0.359 1.527 0.127 -0.155 1.250
Children 0.2135 0.282 0.758 0.449 -0.339 0.766
Cleaning Supplies 0.3703 0.257 1.438 0.150 -0.134 0.875
Clothing 0.6063 0.154 3.930 0.000 0.304 0.909
Coats & Jackets 0.8189 0.149 5.483 0.000 0.526 1.112
Collectibles 0.6432 0.132 4.869 0.000 0.384 0.902
Computers & Tablets -0.1395 0.237 -0.588 0.556 -0.604 0.325
Crochet 0.4376 0.265 1.654 0.098 -0.081 0.956
Daily & Travel items 0.6584 0.229 2.878 0.004 0.210 1.107
Diapering 0.6539 0.217 3.013 0.003 0.228 1.079
Dolls and Miniatures 0.5809 0.284 2.046 0.041 0.024 1.138
Dresses 0.6970 0.157 4.446 0.000 0.390 1.004
Electronics 0.2652 0.620 0.428 0.669 -0.950 1.481
Exercise 1.6832 0.474 3.554 0.000 0.755 2.611
Fan Shop 0.4016 0.472 0.851 0.395 -0.523 1.326
Feeding 0.0754 0.209 0.361 0.718 -0.334 0.485
Footwear 0.6668 0.416 1.602 0.109 -0.149 1.483
Fragrance 0.6423 0.177 3.633 0.000 0.296 0.989
Furniture 0.1438 0.620 0.232 0.817 -1.071 1.358
Gear 0.1294 0.312 0.415 0.678 -0.481 0.740
Geekery 0.9576 0.414 2.314 0.021 0.147 1.769
Girls (4+) 0.2773 0.151 1.838 0.066 -0.018 0.573
Girls 0-24 Mos 0.1320 0.151 0.875 0.381 -0.164 0.428
Girls 2T-5T 0.2078 0.151 1.376 0.169 -0.088 0.504
Glass 0.7679 0.302 2.543 0.011 0.176 1.360
Golf 0.5624 0.306 1.839 0.066 -0.037 1.162
Hair Care 0.8257 0.300 2.753 0.006 0.238 1.414
Health & Baby Care -0.1435 0.249 -0.575 0.565 -0.632 0.345
Holidays 0.1596 0.636 0.251 0.802 -1.087 1.406
Home Appliances 1.2384 0.234 5.284 0.000 0.779 1.698
Home Decor 0.3980 0.236 1.686 0.092 -0.065 0.861
Home Décor 0.5946 0.222 2.680 0.007 0.160 1.029
Housewares 0.6481 0.175 3.708 0.000 0.306 0.991
Jeans 0.4213 0.152 2.766 0.006 0.123 0.720
Jewelry 0.2933 0.198 1.479 0.139 -0.095 0.682
Kids' Home Store 0.5969 0.400 1.492 0.136 -0.187 1.381
Kitchen & Dining 0.8099 0.232 3.495 0.000 0.356 1.264
Knitting 0.8369 0.620 1.350 0.177 -0.378 2.051
Label not given 0.4098 0.106 3.872 0.000 0.202 0.617
Magazines 0.7358 0.305 2.416 0.016 0.139 1.333
Makeup 0.6912 0.231 2.998 0.003 0.239 1.143
Maternity 0.6184 0.150 4.121 0.000 0.324 0.913
Media -0.1145 0.258 -0.444 0.657 -0.620 0.391
Men's Accessories 0.2182 0.141 1.548 0.122 -0.058 0.494
Music 0.7696 0.217 3.547 0.000 0.344 1.195
Musical instruments 1.3198 0.218 6.061 0.000 0.893 1.747
Needlecraft 0.0336 0.359 0.094 0.925 -0.670 0.737
Nursery 0.3432 0.287 1.198 0.231 -0.218 0.905
Office supplies 0.3208 0.201 1.597 0.110 -0.073 0.714
Other 0.4647 0.134 3.461 0.001 0.202 0.728
Others -0.0316 0.275 -0.115 0.909 -0.570 0.507
Outdoors 0.2288 0.237 0.967 0.334 -0.235 0.693
Pants 0.4838 0.145 3.347 0.001 0.200 0.767
Paper Ephemera 0.3717 0.447 0.832 0.405 -0.504 1.247
Paper Goods 0.6451 0.483 1.336 0.182 -0.301 1.592
Patterns 0.5732 0.827 0.693 0.488 -1.048 2.194
Pet Supplies 0.5276 0.166 3.179 0.001 0.202 0.853
Pets 0.4561 0.338 1.348 0.178 -0.207 1.119
Potty Training 0.4583 0.237 1.934 0.053 -0.006 0.923
Pregnancy & Maternity -0.1326 0.391 -0.339 0.735 -0.900 0.635
Quilts 0.2967 0.358 0.829 0.407 -0.404 0.998
Safety -0.1094 0.626 -0.175 0.861 -1.337 1.118
Seasonal Décor 0.4087 0.631 0.648 0.517 -0.828 1.646
Serving 0.4007 0.555 0.721 0.471 -0.688 1.489
Shoes 0.8076 0.163 4.962 0.000 0.489 1.127
Shorts 0.0792 0.179 0.442 0.659 -0.272 0.430
Skin Care 0.7488 0.231 3.248 0.001 0.297 1.201
Skirts 0.2988 0.184 1.626 0.104 -0.061 0.659
Storage & Organization 0.7245 0.347 2.086 0.037 0.044 1.405
Strollers 0.8092 0.262 3.086 0.002 0.295 1.323
Suits 0.7141 0.639 1.117 0.264 -0.538 1.967
Suits & Blazers 0.3156 0.337 0.935 0.350 -0.346 0.977
Supplies 0.6611 0.154 4.288 0.000 0.359 0.963
Sweaters 0.5695 0.154 3.694 0.000 0.267 0.872
Sweats & Hoodies 0.0998 0.328 0.304 0.761 -0.543 0.743
Swimwear 0.6583 0.201 3.274 0.001 0.264 1.052
TV, Audio & Surveillance 0.8178 0.346 2.363 0.018 0.139 1.496
Team Sports 0.7668 0.255 3.004 0.003 0.267 1.267
Tools & Accessories 0.6973 0.222 3.144 0.002 0.263 1.132
Tops 0.2640 0.213 1.238 0.216 -0.154 0.682
Tops & Blouses 0.5013 0.143 3.495 0.000 0.220 0.782
Toy 0.6889 0.169 4.073 0.000 0.357 1.020
Toys 0.3619 0.156 2.326 0.020 0.057 0.667
Trading Cards 0.6818 0.249 2.733 0.006 0.193 1.171
Underwear 0.2326 0.150 1.555 0.120 -0.061 0.526
Video Games & Consoles 0.2938 0.165 1.782 0.075 -0.029 0.617
Weddings 0.7476 0.185 4.042 0.000 0.385 1.110
Women's Accessories 0.1415 0.140 1.011 0.312 -0.133 0.416
Women's Handbags 0.4887 0.149 3.275 0.001 0.196 0.781
Woodworking 0.5407 0.299 1.808 0.071 -0.046 1.127
50 To 75 Years 0.5531 0.316 1.753 0.080 -0.065 1.172
75 To 100 Years 0.5409 0.344 1.574 0.115 -0.133 1.214
A-Line 0.6685 0.247 2.705 0.007 0.184 1.153
Above Knee, Mini 0.1271 0.211 0.602 0.547 -0.287 0.541
Accessories -0.0315 0.197 -0.160 0.873 -0.418 0.355
Accessory 0.6393 0.747 0.856 0.392 -0.825 2.103
Action Figure 0.1574 0.231 0.681 0.496 -0.295 0.610
Action Figures & Statues 0.2331 0.204 1.140 0.254 -0.167 0.634
Action, Adventure 1.039e-14 6.47e-15 1.605 0.108 -2.3e-15 2.31e-14
Activity Centers & Entertainers -0.3067 0.695 -0.441 0.659 -1.669 1.056
Afghan -1.056e-14 5.34e-15 -1.980 0.048 -2.1e-14 -1.07e-16
Air Conditioners -9.782e-15 5.56e-15 -1.759 0.079 -2.07e-14 1.12e-15
Air Fresheners 0.7160 0.313 2.284 0.022 0.102 1.330
Air Purifiers 0.1626 0.555 0.293 0.770 -0.926 1.251
All Other Sports 0.1981 0.558 0.355 0.722 -0.895 1.291
Amplifiers & Effects 0.3832 0.306 1.250 0.211 -0.217 0.984
Animal -0.0668 0.277 -0.241 0.810 -0.610 0.476
Animals -0.3944 0.547 -0.721 0.471 -1.467 0.678
Animation -0.3481 0.294 -1.184 0.237 -0.925 0.228
Apron -0.0544 0.406 -0.134 0.894 -0.851 0.742
Area Rugs & Pads 0.9677 0.270 3.582 0.000 0.438 1.497
Art -0.1701 0.363 -0.468 0.640 -0.882 0.542
Art Doll -1.531e-14 9.02e-15 -1.698 0.090 -3.3e-14 2.37e-15
Arts & Crafts -0.3310 0.205 -1.615 0.106 -0.733 0.071
Arts & Photography 0.0756 0.294 0.257 0.797 -0.500 0.651
Asymmetrical 0.2764 0.321 0.861 0.389 -0.353 0.905
Asymmetrical Hem 0.8068 0.214 3.770 0.000 0.387 1.226
Athletic 0.7028 0.216 3.261 0.001 0.280 1.125
Athletic Apparel 0.9509 0.413 2.303 0.021 0.142 1.760
Athletic Training -0.5934 0.494 -1.200 0.230 -1.562 0.376
Automotive Enthusiast Merchandise -1.2890 0.557 -2.312 0.021 -2.382 -0.196
Baby 0.7174 0.333 2.155 0.031 0.065 1.370
Baby & Child Care 0.3724 0.326 1.142 0.254 -0.267 1.012
Baby & Toddler Toys 0.2192 0.211 1.041 0.298 -0.194 0.632
Baby Gyms & Playmats 0.6968 0.382 1.825 0.068 -0.051 1.445
Baby Seats 0.7025 0.384 1.828 0.068 -0.051 1.456
Backpack 0.9694 0.537 1.805 0.071 -0.083 2.022
Backpack Style 0.7234 0.207 3.486 0.000 0.317 1.130
Backpacks & Carriers 0.7759 0.341 2.279 0.023 0.108 1.443
Backpacks, Bags & Briefcases 0.8581 0.207 4.143 0.000 0.452 1.264
Baggy, Loose 0.2736 0.324 0.845 0.398 -0.361 0.908
Bags & Cases 0.1871 0.262 0.715 0.474 -0.325 0.700
Bags and Purses 1.0741 0.348 3.089 0.002 0.393 1.756
Baguette 0.1515 0.407 0.372 0.710 -0.647 0.950
Bakeware 0.3239 0.262 1.237 0.216 -0.189 0.837
Band & Orchestra -0.8630 0.406 -2.125 0.034 -1.659 -0.067
Baseball & Softball -0.0941 0.152 -0.618 0.537 -0.393 0.205
Basic Supplies -0.1035 0.247 -0.419 0.676 -0.588 0.381
Basket 1.2726 0.523 2.435 0.015 0.248 2.297
Basketball -0.0987 0.295 -0.334 0.738 -0.677 0.480
Baskets 0.4650 0.295 1.575 0.115 -0.114 1.044
Baskets & Bins 0.2941 0.388 0.759 0.448 -0.465 1.054
Bath 0.2246 0.228 0.985 0.325 -0.223 0.672
Bath Linen Sets 0.8178 0.770 1.062 0.288 -0.692 2.328
Bath Rugs 0.9264 0.772 1.199 0.230 -0.587 2.440
Bathing Accessories 0.3506 0.252 1.390 0.164 -0.144 0.845
Bathing Tubs & Seats 0.9476 0.536 1.767 0.077 -0.103 1.999
Bathroom Accessories 0.3573 0.480 0.744 0.457 -0.584 1.299
Bathroom Furniture Sets 1.028e-14 9.39e-15 1.095 0.273 -8.12e-15 2.87e-14
Bathroom Storage & Organization 0.2878 0.562 0.512 0.609 -0.814 1.389
Batteries -0.4237 0.338 -1.252 0.211 -1.087 0.240
Beach Accessories 0.0974 0.108 0.900 0.368 -0.115 0.310
Bead -0.3088 0.294 -1.051 0.293 -0.885 0.267
Bed 5.295e-15 7.1e-15 0.746 0.456 -8.62e-15 1.92e-14
Bed Pillows 0.8072 0.674 1.197 0.231 -0.514 2.129
Bed in a Bag 1.1279 0.770 1.464 0.143 -0.382 2.638
Bedding 0.4582 0.320 1.434 0.152 -0.168 1.084
Bedroom Furniture 2.2962 0.890 2.580 0.010 0.552 4.041
Bedspreads & Coverlets 0.9700 0.729 1.330 0.183 -0.459 2.399
Belt 0.8757 0.478 1.831 0.067 -0.062 1.813
Belts 0.5319 0.203 2.615 0.009 0.133 0.931
Bicycle Child Seats & Trailers 0.1719 0.706 0.243 0.808 -1.212 1.556
Bike & Skate 0.9864 0.286 3.453 0.001 0.426 1.546
Binoculars & Telescopes 0.7378 0.737 1.000 0.317 -0.708 2.183
Biographies & Memoirs 0.0105 0.299 0.035 0.972 -0.577 0.598
Biography -0.2924 0.542 -0.540 0.589 -1.354 0.769
Birthday 0.1489 0.716 0.208 0.835 -1.254 1.552
Blanket 0.9441 0.882 1.070 0.285 -0.785 2.673
Blankets & Throws 1.0753 0.642 1.676 0.094 -0.182 2.333
Blazer 0.5174 0.371 1.396 0.163 -0.209 1.244
Block 0.2621 0.491 0.533 0.594 -0.701 1.225
Blouse 0.0850 0.201 0.422 0.673 -0.310 0.480
Blu-Ray 0.2663 0.287 0.929 0.353 -0.295 0.828
Board Shorts 0.1011 0.185 0.546 0.585 -0.262 0.464
Board, Surf 0.6009 0.380 1.579 0.114 -0.145 1.347
Boating 9.648e-15 8.3e-15 1.163 0.245 -6.62e-15 2.59e-14
Body 0.1972 0.252 0.782 0.434 -0.297 0.691
Book -0.1951 0.196 -0.995 0.320 -0.580 0.189
Bookmark -0.3838 0.633 -0.606 0.545 -1.625 0.858
Boot Cut 0.4742 0.209 2.265 0.023 0.064 0.884
Boots 0.4658 0.216 2.160 0.031 0.043 0.888
Bottle-Feeding 0.2462 0.251 0.980 0.327 -0.246 0.739
Bottles 0.0122 0.368 0.033 0.974 -0.709 0.733
Bottoms 0.2185 0.199 1.100 0.271 -0.171 0.608
Bouquets -0.0220 0.426 -0.052 0.959 -0.857 0.813
Bowl 0.3815 0.262 1.455 0.146 -0.132 0.895
Bowls -0.2126 0.487 -0.437 0.662 -1.166 0.741
Box 0.9906 0.450 2.200 0.028 0.108 1.873
Boxing & MMA -0.6555 0.521 -1.257 0.209 -1.678 0.367
Boyfriend 0.7000 0.222 3.146 0.002 0.264 1.136
Boys 0.4701 0.221 2.130 0.033 0.037 0.903
Bracelet 0.6657 0.273 2.436 0.015 0.130 1.201
Bracelets 0.1803 0.238 0.758 0.448 -0.286 0.646
Bras 0.3923 0.206 1.907 0.057 -0.011 0.795
Brass Instruments 9.328e-15 6.15e-15 1.517 0.129 -2.73e-15 2.14e-14
Breastfeeding 0.5327 0.268 1.991 0.047 0.008 1.057
Breastfeeding Pillows & Stools 0.7904 0.436 1.812 0.070 -0.065 1.645
Brooch 0.5704 0.262 2.173 0.030 0.056 1.085
Brushes 9.942e-15 8.3e-15 1.198 0.231 -6.32e-15 2.62e-14
Brushes & Applicators -0.0156 0.255 -0.061 0.951 -0.515 0.484
Buckle 0.4849 0.478 1.014 0.311 -0.452 1.422
Building Toys 0.1617 0.223 0.724 0.469 -0.276 0.599
Business & Money 0.5025 0.356 1.412 0.158 -0.195 1.200
Button Down Shirt 0.0597 0.204 0.292 0.770 -0.341 0.460
Button-Front 0.3501 0.260 1.344 0.179 -0.160 0.860
CD 0.0880 0.287 0.306 0.759 -0.475 0.651
Cabinet Locks & Straps 0.6327 0.786 0.805 0.421 -0.908 2.173
Cables & Adapters -0.7396 0.294 -2.519 0.012 -1.315 -0.164
Cabochon -0.3223 0.366 -0.881 0.379 -1.040 0.395
Cage 0.5415 0.154 3.523 0.000 0.240 0.843
Cake Toppers 0.1415 0.426 0.332 0.740 -0.694 0.977
Calendars 0.0445 0.238 0.187 0.852 -0.422 0.511
Camcorders 1.9367 0.691 2.803 0.005 0.583 3.291
Camera 1.0950 0.661 1.657 0.098 -0.200 2.390
Camera & Photo Accessories 1.4539 0.649 2.241 0.025 0.182 2.726
Candle Holder 0.1240 0.450 0.275 0.783 -0.759 1.006
Candles & Holders 0.6334 0.251 2.528 0.011 0.142 1.124
Candles & Home Scents 0.5324 0.217 2.456 0.014 0.108 0.957
Cape 0.1188 0.345 0.345 0.730 -0.557 0.794
Capri, Cropped 0.3140 0.217 1.450 0.147 -0.110 0.738
Capris, Cropped 0.2621 0.208 1.259 0.208 -0.146 0.670
Car 0.0364 0.261 0.140 0.889 -0.475 0.548
Car Care -0.5089 0.330 -1.542 0.123 -1.156 0.138
Car Electronics & Accessories 0.1767 0.145 1.215 0.224 -0.108 0.462
Car Seats 0.5781 0.264 2.190 0.029 0.061 1.095
Car Security & Convenience 0.4363 0.548 0.796 0.426 -0.638 1.511
Car Speakers & Systems -9.894e-15 8.78e-15 -1.127 0.260 -2.71e-14 7.31e-15
Car Stereos & Components 0.0951 0.426 0.223 0.823 -0.739 0.929
Car Subwoofers 1.1607 0.548 2.117 0.034 0.086 2.235
Car Video 0.4045 0.340 1.190 0.234 -0.262 1.071
Cardigan 0.4049 0.210 1.929 0.054 -0.006 0.816
Cards 0.2067 0.510 0.405 0.685 -0.794 1.207
Cargo 0.3749 0.231 1.625 0.104 -0.077 0.827
Carpenter 0.0821 0.490 0.167 0.867 -0.879 1.043
Carpenter, Utility 0.8497 0.652 1.304 0.192 -0.427 2.127
Case 1.6675 0.797 2.092 0.036 0.105 3.230
Cases, Covers & Skins -0.5852 0.291 -2.010 0.044 -1.156 -0.015
Casserole 1.1521 0.836 1.378 0.168 -0.487 2.791
Casual Pants 0.2484 0.206 1.203 0.229 -0.156 0.653
Casual Shorts 0.5732 0.242 2.364 0.018 0.098 1.049
Cell Phone Accessories -0.4804 0.292 -1.646 0.100 -1.052 0.092
Cell Phones & Smartphones 0.9922 0.292 3.401 0.001 0.420 1.564
Ceramic 0.5312 0.251 2.114 0.035 0.039 1.024
Chain 0.4568 0.656 0.697 0.486 -0.829 1.742
Change Purse 0.3374 0.531 0.635 0.525 -0.704 1.379
Changing Pads & Covers -0.3049 0.318 -0.958 0.338 -0.928 0.319
Chargers & Cradles -0.5858 0.295 -1.985 0.047 -1.164 -0.007
Charm 0.5169 0.224 2.309 0.021 0.078 0.956
Children -0.4540 0.357 -1.272 0.203 -1.154 0.246
Children's Books 0.1382 0.238 0.581 0.561 -0.328 0.605
Christian Books & Bibles 0.1841 0.269 0.685 0.493 -0.342 0.711
Christmas 0.6437 0.641 1.004 0.315 -0.612 1.900
Classic, Straight Leg 0.4907 0.215 2.287 0.022 0.070 0.911
Cleansers -0.1370 0.240 -0.571 0.568 -0.608 0.334
Cleats 0.6368 0.434 1.468 0.142 -0.213 1.487
Clocks 0.3958 0.281 1.410 0.159 -0.154 0.946
Cloth Diapers 0.1129 0.141 0.802 0.422 -0.163 0.389
Clothing 0.8498 0.400 2.123 0.034 0.065 1.634
Clothing & Closet Storage 0.4210 0.379 1.111 0.267 -0.322 1.164
Clutch 0.6054 0.530 1.141 0.254 -0.434 1.645
Coaster 0.2171 0.420 0.517 0.605 -0.606 1.041
Coats & Jackets 0.3203 0.199 1.613 0.107 -0.069 0.709
Coffee & Tea Accessories 0.2554 0.254 1.004 0.316 -0.243 0.754
Collages 0.2498 0.654 0.382 0.703 -1.033 1.532
Collar 0.1765 0.423 0.417 0.677 -0.653 1.006
Collared 0.5189 0.231 2.249 0.025 0.067 0.971
Collectibles 0.0709 0.163 0.434 0.664 -0.249 0.391
Comforters & Sets 1.4215 0.647 2.198 0.028 0.154 2.689
Comic -0.3144 0.400 -0.787 0.431 -1.098 0.469
Comics 0.3140 0.337 0.931 0.352 -0.347 0.975
Components & Parts 0.6855 0.274 2.506 0.012 0.149 1.222
Conditioners 0.4169 0.332 1.256 0.209 -0.234 1.067
Consoles 1.2119 0.204 5.946 0.000 0.812 1.611
Cookbook 0.1088 0.427 0.255 0.799 -0.728 0.946
Cookware 0.8005 0.276 2.896 0.004 0.259 1.342
Corduroys 0.0141 0.365 0.039 0.969 -0.702 0.730
Corset 0.3394 0.242 1.401 0.161 -0.135 0.814
Cosmetic Bags -0.1539 0.208 -0.738 0.460 -0.563 0.255
Costume 0.3118 0.230 1.356 0.175 -0.139 0.762
Cotton & Swabs 1.416e-14 1.08e-14 1.308 0.191 -7.06e-15 3.54e-14
Cover-Ups 0.0019 0.108 0.017 0.986 -0.210 0.214
Cowl Neck 0.5932 0.227 2.616 0.009 0.149 1.038
Crafting -0.8746 0.692 -1.265 0.206 -2.230 0.481
Cream and Sugar Set 0.3731 0.716 0.521 0.602 -1.029 1.776
Crewneck 0.3388 0.210 1.613 0.107 -0.073 0.750
Crochet 1.779e-14 8.49e-15 2.097 0.036 1.16e-15 3.44e-14
Cross Stitch 0.0441 0.579 0.076 0.939 -1.090 1.178
Cuff 4.506e-15 6.8e-15 0.663 0.508 -8.82e-15 1.78e-14
Cuff Links 0.9588 0.479 2.000 0.046 0.019 1.898
Cup 0.2904 0.262 1.110 0.267 -0.222 0.803
Custom 0.1417 0.472 0.300 0.764 -0.784 1.068
DJ, Electronic Music & Karaoke 0.1236 0.234 0.528 0.598 -0.335 0.583
DVD 0.0484 0.283 0.171 0.864 -0.506 0.603
DVD & Blu-ray Players -0.3164 0.401 -0.788 0.430 -1.103 0.470
Dance/Ballet -1.1207 0.485 -2.312 0.021 -2.071 -0.171
Day of the Dead 0.9595 0.729 1.316 0.188 -0.470 2.389
Decorations 0.0818 0.279 0.294 0.769 -0.465 0.628
Decorative Pillows 0.4391 0.256 1.718 0.086 -0.062 0.940
Decorative Pillows, Inserts & Covers 0.2506 0.655 0.383 0.702 -1.033 1.535
Denim 0.7758 0.383 2.027 0.043 0.026 1.526
Desktops & All-In-Ones 1.6561 0.335 4.943 0.000 0.999 2.313
Diaper Bag -2.502e-14 9.79e-15 -2.556 0.011 -4.42e-14 -5.84e-15
Diaper Bags 0.8401 0.129 6.521 0.000 0.588 1.093
Diaper Pails & Refills -0.1928 0.543 -0.355 0.723 -1.257 0.872
Digital Cameras 2.0378 0.652 3.127 0.002 0.761 3.315
Dining & Entertaining 0.3409 0.255 1.336 0.182 -0.159 0.841
Dinnerware Set 3.567e-15 1.35e-14 0.264 0.791 -2.29e-14 3e-14
Dishes -0.1282 0.695 -0.184 0.854 -1.491 1.235
Disposable Diapers 0.1503 0.147 1.025 0.306 -0.137 0.438
Dogs 0.0071 0.082 0.087 0.930 -0.153 0.167
Doll 0.3434 0.215 1.595 0.111 -0.079 0.765
Doll Clothing -1.2117 1.037 -1.169 0.243 -3.244 0.821
Dolls & Accessories 0.3557 0.204 1.740 0.082 -0.045 0.756
Doormats 0.2864 0.655 0.437 0.662 -0.998 1.571
Double Breasted -2.072e-14 1.02e-14 -2.028 0.043 -4.08e-14 -6.95e-16
Drawings 0.2494 0.338 0.738 0.461 -0.413 0.912
Dress 1.0108 0.294 3.444 0.001 0.436 1.586
Dress - Flat Front 0.0904 0.301 0.300 0.764 -0.500 0.681
Dress - Pleat 0.3681 0.733 0.503 0.615 -1.068 1.804
Dress Pants 0.2999 0.212 1.418 0.156 -0.115 0.715
Dress Shirts 0.4029 0.277 1.452 0.146 -0.141 0.946
Dress Shorts 0.3378 0.357 0.947 0.344 -0.361 1.037
Dress Suit 1.4080 0.531 2.650 0.008 0.367 2.449
Dress Up & Pretend Play 0.2074 0.214 0.967 0.334 -0.213 0.628
Dresses 0.4092 0.198 2.064 0.039 0.021 0.798
Drives, Storage & Media 0.3560 0.286 1.244 0.214 -0.205 0.917
Drums & Percussion 0.7164 0.565 1.268 0.205 -0.391 1.824
Dusting 0.6539 0.672 0.973 0.330 -0.663 1.970
Duvet Covers & Sets 1.3373 0.660 2.027 0.043 0.044 2.630
Earrings -0.0132 0.238 -0.056 0.956 -0.479 0.452
Easter 0.5469 0.663 0.825 0.409 -0.753 1.846
Edge & Corner Guards 0.0657 0.882 0.074 0.941 -1.664 1.795
Education & Teaching 0.6503 0.239 2.717 0.007 0.181 1.120
Educational 2.3383 0.686 3.411 0.001 0.995 3.682
Electronic -0.2344 0.260 -0.902 0.367 -0.744 0.275
Electronics 1.0073 0.214 4.703 0.000 0.587 1.427
Electronics for Kids 0.7259 0.225 3.221 0.001 0.284 1.168
Embroidery 0.6767 0.473 1.430 0.153 -0.251 1.604
Engineering & Transportation 0.2315 0.650 0.356 0.722 -1.043 1.506
Entertainment -0.0067 0.885 -0.008 0.994 -1.741 1.728
Epilators -0.0677 0.390 -0.174 0.862 -0.831 0.696
Exterior Accessories -0.5145 0.112 -4.576 0.000 -0.735 -0.294
Eyes 0.1223 0.251 0.486 0.627 -0.371 0.615
Eyewear 0.7195 0.257 2.798 0.005 0.215 1.224
Fabric -0.0336 0.318 -0.106 0.916 -0.657 0.590
Face 0.2872 0.251 1.144 0.253 -0.205 0.779
Family Planning Tests 0.4772 0.436 1.096 0.273 -0.377 1.331
Fans -0.0529 0.228 -0.233 0.816 -0.499 0.393
Fashion Dolls Apparel -7.506e-15 9.1e-15 -0.825 0.410 -2.53e-14 1.03e-14
Fashion Sneakers 0.5398 0.216 2.495 0.013 0.116 0.964
Favors 0.0824 0.346 0.238 0.812 -0.596 0.761
Feather Beds 1.6372 0.882 1.856 0.063 -0.092 3.366
Feet 0.1735 0.274 0.633 0.526 -0.363 0.711
Fiction -0.2144 0.381 -0.562 0.574 -0.962 0.533
Figurine 0.3725 0.211 1.764 0.078 -0.041 0.786
Film Photography 1.5558 0.645 2.412 0.016 0.291 2.820
Finding -0.3682 0.656 -0.561 0.575 -1.653 0.917
Fishing 0.3153 0.264 1.196 0.232 -0.202 0.832
Fitness accessories -1.0953 0.475 -2.307 0.021 -2.026 -0.165
Fitness technology -0.6237 0.476 -1.311 0.190 -1.556 0.309
Fixture 1.654e-14 9.1e-15 1.816 0.069 -1.31e-15 3.44e-14
Flare 0.4984 0.223 2.238 0.025 0.062 0.935
Flashes & Flash Accessories 1.7080 0.778 2.195 0.028 0.183 3.233
Flats 0.0248 0.217 0.114 0.909 -0.401 0.450
Fleece Jacket 0.2456 0.209 1.174 0.240 -0.164 0.656
Flight/Bomber 0.3864 0.266 1.453 0.146 -0.135 0.908
Food 0.1386 0.265 0.523 0.601 -0.381 0.658
Food Service Equipment & Supplies -0.2474 0.326 -0.758 0.448 -0.887 0.392
Football -0.1752 0.170 -1.033 0.302 -0.508 0.157
Formal 2.0279 0.582 3.484 0.000 0.887 3.169
Frame 0.4496 0.450 0.998 0.318 -0.433 1.332
Full Skirt 0.4379 0.260 1.685 0.092 -0.072 0.947
Full Zip 0.3709 0.213 1.745 0.081 -0.046 0.787
Full-Length 0.5550 0.213 2.606 0.009 0.138 0.972
Furniture 0.8593 0.436 1.972 0.049 0.005 1.713
G-Strings & Thongs 0.0854 0.212 0.402 0.688 -0.331 0.502
GPS Accessories & Mounts -1.0793 0.402 -2.683 0.007 -1.868 -0.291
GPS Units & Equipment 0.2787 0.238 1.170 0.242 -0.188 0.746
Gadget 0.1693 0.611 0.277 0.782 -1.027 1.366
Gadgets 0.1165 0.370 0.315 0.753 -0.609 0.842
Game 0.3560 0.297 1.197 0.231 -0.227 0.939
Games -0.0529 0.201 -0.264 0.792 -0.446 0.340
Garage Storage & Organization 0.0260 0.708 0.037 0.971 -1.361 1.413
Garment Steamers -0.1005 0.267 -0.376 0.707 -0.624 0.423
Gates & Doorways 0.3070 0.883 0.348 0.728 -1.423 2.037
Gift Sets -0.4224 0.438 -0.966 0.334 -1.280 0.435
Gift Wrap -0.3732 0.555 -0.672 0.501 -1.461 0.714
Girls 0.1494 0.227 0.658 0.510 -0.296 0.594
Glass 0.3693 0.211 1.752 0.080 -0.044 0.783
Glassware 0.2088 0.377 0.554 0.579 -0.530 0.947
Gloves 0.4362 0.245 1.781 0.075 -0.044 0.916
Golf Apparel 0.2884 0.346 0.833 0.405 -0.390 0.967
Golf Bags 1.8879 0.912 2.070 0.038 0.100 3.676
Golf Balls 0.2419 0.431 0.561 0.575 -0.603 1.087
Golf Shoes 1.3024 0.677 1.923 0.054 -0.025 2.630
Graphic Design 0.0316 0.534 0.059 0.953 -1.015 1.078
Grooming 0.6708 0.568 1.180 0.238 -0.443 1.785
Guest Books -0.1315 0.654 -0.201 0.841 -1.414 1.151
Guitars -0.1493 0.263 -0.569 0.569 -0.664 0.365
Hair 1.5854 0.215 7.363 0.000 1.163 2.007
Hair & Scalp Treatments 0.1892 0.322 0.587 0.557 -0.443 0.821
Hair Accessories 0.4461 0.201 2.221 0.026 0.052 0.840
Hair Color -0.0099 0.331 -0.030 0.976 -0.659 0.639
Hair Loss Products 0.3399 0.391 0.870 0.384 -0.426 1.106
Hair Perms & Texturizers -0.1180 0.686 -0.172 0.863 -1.462 1.226
Hair Relaxers -0.8112 0.686 -1.183 0.237 -2.155 0.532
Hair Styling Tools 0.7956 0.247 3.226 0.001 0.312 1.279
Halloween 0.6423 0.641 1.002 0.316 -0.614 1.899
Halter -0.0048 0.210 -0.023 0.982 -0.416 0.406
Handbag 1.4796 0.517 2.862 0.004 0.466 2.493
Handmade -1.0558 1.037 -1.018 0.309 -3.088 0.977
Hands & Nails -0.0841 0.271 -0.310 0.756 -0.615 0.447
Harnesses & Leashes 0.2526 0.708 0.357 0.721 -1.135 1.640
Hat 0.5654 0.283 1.996 0.046 0.010 1.121
Hats 0.3196 0.199 1.605 0.109 -0.071 0.710
Hawaiian 0.6479 0.435 1.490 0.136 -0.204 1.500
Headphones -0.9190 0.367 -2.505 0.012 -1.638 -0.200
Headsets -0.2475 0.296 -0.836 0.403 -0.827 0.332
Health Care 0.1723 0.272 0.633 0.527 -0.362 0.706
Henley 0.4407 0.358 1.233 0.218 -0.260 1.141
Highchairs & Booster Seats 0.7252 0.370 1.961 0.050 0.000 1.450
Hiking & Camping 0.5180 0.245 2.117 0.034 0.038 0.998
Hip Bag 0.8507 0.557 1.528 0.126 -0.240 1.941
Historical, Military 1.1740 0.675 1.739 0.082 -0.149 2.497
History 0.5742 0.312 1.840 0.066 -0.037 1.186
Hobbies 0.0461 0.207 0.223 0.824 -0.359 0.452
Hobo 1.0017 0.247 4.052 0.000 0.517 1.486
Hockey 1.3506 0.560 2.413 0.016 0.254 2.447
Home Audio -0.4558 0.491 -0.929 0.353 -1.417 0.506
Home Bar Furniture 2.458e-15 5.38e-15 0.457 0.648 -8.09e-15 1.3e-14
Home Brewing & Wine Making 0.4260 0.659 0.647 0.518 -0.865 1.717
Home Decor 0.2950 0.268 1.100 0.271 -0.230 0.820
Home Décor Accents 0.3963 0.244 1.622 0.105 -0.083 0.875
Home Entertainment Furniture 1.5164 0.890 1.704 0.088 -0.228 3.261
Home Fragrance 0.5197 0.248 2.099 0.036 0.034 1.005
Home Office Furniture 0.5561 0.890 0.625 0.532 -1.188 2.301
Home Speakers & Subwoofers -0.6878 0.396 -1.738 0.082 -1.463 0.088
Home Surveillance -0.0680 0.400 -0.170 0.865 -0.852 0.716
Hooded 0.3735 0.210 1.781 0.075 -0.038 0.785
Hoodie 0.8547 0.358 2.384 0.017 0.152 1.557
Household Cleaners 0.9709 0.290 3.351 0.001 0.403 1.539
Household Supplies 0.0684 0.300 0.228 0.819 -0.519 0.655
Housewares 4.727e-15 6.43e-15 0.735 0.463 -7.88e-15 1.73e-14
How to -0.2728 0.692 -0.394 0.693 -1.628 1.083
Human Figure Doll -0.2271 0.547 -0.415 0.678 -1.299 0.845
Humidifiers -0.0685 0.555 -0.123 0.902 -1.157 1.020
Humidifiers & Vaporizers 0.9480 0.354 2.680 0.007 0.255 1.641
Humor 1.93e-15 5.82e-15 0.332 0.740 -9.47e-15 1.33e-14
Illustration 0.4837 0.493 0.981 0.326 -0.483 1.450
Indoor/Outdoor Games 0.8958 0.261 3.431 0.001 0.384 1.408
Ink & Toner 0.3518 0.250 1.408 0.159 -0.138 0.842
Instructional 0.2644 0.692 0.382 0.702 -1.091 1.620
Instrument 1.0843 0.364 2.975 0.003 0.370 1.799
Instrument Accessories -0.9159 0.181 -5.069 0.000 -1.270 -0.562
Interior Accessories 0.1902 0.140 1.358 0.175 -0.084 0.465
Irons & Ironing Boards -0.3360 0.333 -1.009 0.313 -0.989 0.317
Jacket 0.6374 0.332 1.922 0.055 -0.013 1.287
Jackets 0.5544 0.213 2.603 0.009 0.137 0.972
Jean Jacket 0.2486 0.214 1.164 0.245 -0.170 0.667
Jeans 0.2857 0.205 1.393 0.164 -0.116 0.688
Jerseys 0.5962 0.215 2.767 0.006 0.174 1.019
Jewelry -0.0292 0.230 -0.127 0.899 -0.481 0.422
Jewelry Boxes & Organizers 0.2163 0.367 0.590 0.555 -0.503 0.935
Joggers -1.918e-14 8.1e-15 -2.368 0.018 -3.51e-14 -3.3e-15
Journal -0.5012 0.510 -0.983 0.325 -1.500 0.498
Keyboards 0.0527 0.234 0.225 0.822 -0.406 0.512
Keychain 0.2046 0.212 0.964 0.335 -0.212 0.621
Khakis, Chinos 0.2536 0.216 1.174 0.240 -0.170 0.677
Kids -0.2531 0.283 -0.896 0.371 -0.807 0.301
Kids' Bedding 0.9546 0.438 2.179 0.029 0.096 1.813
Kids' Flatware 1.083e-14 6.84e-15 1.584 0.113 -2.57e-15 2.42e-14
Kids' Furniture 1.1857 0.541 2.190 0.029 0.125 2.247
Kids' Furniture, Décor & Storage 0.1608 0.295 0.544 0.586 -0.418 0.740
Kids' Room Décor 0.0907 0.430 0.211 0.833 -0.753 0.934
Kitchen 0.7113 0.427 1.664 0.096 -0.126 1.549
Kitchen & Table Linens 0.0378 0.277 0.136 0.891 -0.505 0.581
Kitchen Appliances 0.4356 0.144 3.031 0.002 0.154 0.717
Kitchen Knives & Cutlery Accessories 0.7742 0.301 2.575 0.010 0.185 1.364
Kitchen Safety 0.2520 0.737 0.342 0.732 -1.192 1.696
Kitchen Storage & Organization 0.8733 0.708 1.234 0.217 -0.514 2.260
Kitchen Utensils & Gadgets 0.1027 0.258 0.399 0.690 -0.402 0.608
Knee-Length 0.3547 0.211 1.679 0.093 -0.059 0.769
Knit Top 0.2363 0.205 1.154 0.248 -0.165 0.638
Knitting 0.7372 1.037 0.711 0.477 -1.295 2.770
Knitting Supplies -0.1326 0.671 -0.198 0.843 -1.448 1.182
Label not given 0.4098 0.106 3.872 0.000 0.202 0.617
Lacrosse 0.1000 0.558 0.179 0.858 -0.993 1.193
Lamps& Accessories 0.5551 0.258 2.153 0.031 0.050 1.060
Lanyard 0.4550 0.214 2.126 0.034 0.036 0.874
Laptop 1.8848 0.796 2.368 0.018 0.325 3.445
Laptops & Netbooks 1.7500 0.269 6.496 0.000 1.222 2.278
Laundry Storage & Organization 0.0321 0.504 0.064 0.949 -0.956 1.020
Learning & Education 0.1677 0.226 0.742 0.458 -0.275 0.611
Leash 0.6973 0.713 0.978 0.328 -0.700 2.094
Leather 0.2024 0.641 0.316 0.752 -1.054 1.459
Leg Warmers 0.4382 0.281 1.558 0.119 -0.113 0.990
Leggings 0.7006 0.209 3.349 0.001 0.291 1.111
Lenses & Filters 1.5057 0.670 2.246 0.025 0.192 2.820
Lifestyle & Cultures -0.2038 0.375 -0.544 0.587 -0.939 0.531
Lighting 5.149e-15 4.67e-15 1.103 0.270 -4e-15 1.43e-14
Lighting & Studio 1.2864 0.737 1.744 0.081 -0.159 2.732
Lights & Lighting Accessories 0.2744 0.128 2.144 0.032 0.024 0.525
Lightweight 2.7578 0.883 3.124 0.002 1.028 4.488
Linen 0.3700 0.288 1.285 0.199 -0.195 0.934
Lingerie 0.2126 0.228 0.930 0.352 -0.235 0.660
Lips 0.1775 0.251 0.706 0.480 -0.315 0.670
Literature & Fiction 0.0475 0.233 0.204 0.838 -0.409 0.504
Lithographs, Etchings & Woodcuts 0.9224 0.539 1.713 0.087 -0.133 1.978
Live Sound & Stage 1.044e-15 5.15e-15 0.203 0.839 -9.05e-15 1.11e-14
Living Room Furniture 1.9701 0.781 2.522 0.012 0.439 3.501
Loafers & Slip-Ons 0.1084 0.218 0.498 0.619 -0.319 0.535
Luggage 1.6467 0.527 3.127 0.002 0.615 2.679
MLB 0.0637 0.477 0.133 0.894 -0.872 0.999
Magic 1.2953 0.391 3.315 0.001 0.529 2.061
Magnet -0.7859 0.488 -1.610 0.107 -1.743 0.171
Magnets -0.3184 0.652 -0.488 0.625 -1.597 0.960
Makeup Brushes & Tools 0.1547 0.245 0.632 0.528 -0.325 0.635
Makeup Palettes 0.5647 0.252 2.244 0.025 0.071 1.058
Makeup Remover 0.2448 0.280 0.876 0.381 -0.303 0.793
Makeup Sets 0.4884 0.253 1.933 0.053 -0.007 0.984
Maternity -0.2771 0.658 -0.421 0.674 -1.567 1.013
Maternity Pillows 1.7075 0.734 2.325 0.020 0.268 3.147
Mattress Pads 0.6814 0.708 0.963 0.336 -0.706 2.069
Maxi 0.6601 0.236 2.794 0.005 0.197 1.123
Medical Books 0.7695 0.258 2.986 0.003 0.264 1.275
Medical Supplies & Equipment 0.5944 0.279 2.129 0.033 0.047 1.142
Men 0.7348 0.214 3.428 0.001 0.315 1.155
Men's Golf Clubs 1.2220 0.382 3.196 0.001 0.473 1.971
Messenger & Crossbody 0.6606 0.206 3.204 0.001 0.256 1.065
Microphones & Accessories -0.1389 0.222 -0.626 0.532 -0.574 0.296
Mid-Calf 0.4748 0.223 2.130 0.033 0.038 0.912
Military 0.4443 0.226 1.964 0.050 0.001 0.888
Mini 0.2957 0.235 1.257 0.209 -0.165 0.757
Miniature 1.2879 0.547 2.354 0.019 0.216 2.360
Mirrors 0.5886 0.258 2.280 0.023 0.083 1.095
Monitors 2.0649 0.661 3.122 0.002 0.769 3.361
Mopping 1.3835 0.516 2.682 0.007 0.372 2.395
Motorcycle 0.3807 0.216 1.759 0.079 -0.043 0.805
Motorcycle & Powersports 1.3787 0.242 5.702 0.000 0.905 1.853
Mug 0.6125 0.580 1.057 0.291 -0.524 1.749
Mules & Clogs 0.4175 0.231 1.807 0.071 -0.035 0.870
Music 0.6155 0.489 1.260 0.208 -0.342 1.573
NBA 0.1485 0.481 0.309 0.758 -0.794 1.091
NCAA 0.1370 0.478 0.287 0.774 -0.800 1.074
NFL 0.1074 0.474 0.226 0.821 -0.822 1.037
NHL 0.4556 0.497 0.916 0.359 -0.519 1.430
Nail Care 1.1237 0.669 1.678 0.093 -0.189 2.436
Nail Tools 0.1453 0.265 0.548 0.583 -0.374 0.664
Nails 0.0526 0.253 0.208 0.835 -0.443 0.548
Nasal Aspirators 0.1069 0.477 0.224 0.823 -0.829 1.042
Necklace 0.6390 0.258 2.472 0.013 0.132 1.146
Necklaces 0.0393 0.237 0.165 0.869 -0.426 0.505
Networking & Connectivity 0.6725 0.323 2.083 0.037 0.040 1.305
Nonfiction -0.0954 0.542 -0.176 0.860 -1.157 0.966
Notebook -0.1950 0.541 -0.361 0.718 -1.255 0.865
Novelty 1.0756 0.797 1.349 0.177 -0.487 2.638
Novelty & Gag Toys -0.9228 0.365 -2.526 0.012 -1.639 -0.207
Nursery Décor 0.2860 0.339 0.843 0.399 -0.379 0.951
One Button -1.2611 1.056 -1.194 0.233 -3.332 0.810
One-Piece 0.1788 0.093 1.920 0.055 -0.004 0.361
One-Pieces 0.3144 0.198 1.588 0.112 -0.074 0.702
Organization 0.4817 0.239 2.015 0.044 0.013 0.950
Origami 0.2684 0.785 0.342 0.732 -1.270 1.807
Ornaments -0.8408 0.487 -1.728 0.084 -1.795 0.113
Other 0.2901 0.195 1.485 0.138 -0.093 0.673
Other Accessories -0.8407 0.340 -2.472 0.013 -1.507 -0.174
Other Furniture 1.0008 0.890 1.124 0.261 -0.744 2.745
Others -0.0210 0.088 -0.239 0.811 -0.193 0.151
Outdoor 0.7498 0.374 2.006 0.045 0.017 1.482
Outdoor Safety 0.2536 0.882 0.287 0.774 -1.476 1.983
Outerwear 0.0915 0.483 0.190 0.850 -0.855 1.038
Overalls 0.4537 0.238 1.910 0.056 -0.012 0.919
Oxfords 0.1595 0.246 0.648 0.517 -0.323 0.642
Pacifiers & Accessories 0.1011 0.258 0.392 0.695 -0.404 0.606
Pad 1.646e-15 5.1e-15 0.323 0.747 -8.34e-15 1.16e-14
Painting 0.6101 0.282 2.161 0.031 0.057 1.164
Paintings 0.3568 0.360 0.992 0.321 -0.348 1.061
Pant Suit 0.6003 0.379 1.584 0.113 -0.142 1.343
Panties 0.1838 0.207 0.890 0.374 -0.221 0.589
Pants 0.3141 0.218 1.443 0.149 -0.112 0.741
Pants, Tights, Leggings 0.5960 0.212 2.813 0.005 0.181 1.011
Paper -0.0635 0.646 -0.098 0.922 -1.331 1.204
Paper Ephemera 0.3418 0.629 0.543 0.587 -0.891 1.575
Paper Towels 0.7167 0.394 1.820 0.069 -0.055 1.489
Paperweights -0.7132 0.695 -1.026 0.305 -2.076 0.650
Parka 1.0652 0.236 4.509 0.000 0.602 1.528
Party Supplies 0.1604 0.218 0.737 0.461 -0.266 0.587
Patch -0.2357 0.221 -1.069 0.285 -0.668 0.197
Patriotic 1.4793 0.774 1.912 0.056 -0.037 2.995
Pattern 0.3505 0.505 0.694 0.487 -0.639 1.340
Peacoat 0.2258 0.229 0.987 0.324 -0.223 0.674
Pendant 0.5694 0.253 2.254 0.024 0.074 1.064
Performance Parts & Accessories -0.0847 0.330 -0.257 0.798 -0.731 0.562
Personal Care -0.1699 0.271 -0.626 0.531 -0.702 0.362
Personalized 0.1501 0.417 0.360 0.719 -0.667 0.967
Pet Lover 0.2781 0.460 0.604 0.546 -0.624 1.180
Photo Albums & Frames 0.0698 0.264 0.264 0.792 -0.448 0.588
Photographs -0.0183 0.689 -0.027 0.979 -1.368 1.332
Photography 4.49e-16 3.71e-15 0.121 0.904 -6.82e-15 7.72e-15
Pillow 0.4002 0.721 0.555 0.579 -1.013 1.814
Pillows -0.1820 0.654 -0.278 0.781 -1.465 1.101
Pillows & Stools 7.661e-15 4.73e-15 1.620 0.105 -1.61e-15 1.69e-14
Pin 0.0601 0.253 0.238 0.812 -0.435 0.555
Pinback Button 0.0060 0.240 0.025 0.980 -0.464 0.476
Planter 0.3215 0.514 0.625 0.532 -0.686 1.329
Plate 0.7947 0.673 1.181 0.238 -0.524 2.113
Playards 1.6487 0.573 2.879 0.004 0.526 2.771
Pleated 0.8567 0.257 3.338 0.001 0.354 1.360
Plush -0.2088 0.488 -0.428 0.669 -1.166 0.748
Poetry 0.0794 0.542 0.147 0.883 -0.982 1.141
Politics & Social Sciences 0.7281 0.488 1.493 0.135 -0.228 1.684
Polo 0.0245 0.370 0.066 0.947 -0.701 0.750
Polo Shirt 0.0610 0.223 0.273 0.785 -0.377 0.499
Polo, Rugby 0.4785 0.260 1.841 0.066 -0.031 0.988
Poncho 0.5698 0.237 2.403 0.016 0.105 1.035
Porcelain 0.3487 0.270 1.293 0.196 -0.180 0.877
Portable Audio & Accessories -0.3834 0.369 -1.040 0.298 -1.106 0.339
Postcard -0.0787 0.770 -0.102 0.919 -1.588 1.431
Poster -0.2441 0.364 -0.670 0.503 -0.958 0.470
Posters & Prints -0.1063 0.332 -0.320 0.749 -0.758 0.545
Potties & Seats 0.0723 0.242 0.299 0.765 -0.401 0.546
Pouch 0.5141 0.542 0.948 0.343 -0.548 1.577
Prams 5.403e-15 5.62e-15 0.961 0.337 -5.62e-15 1.64e-14
Prenatal Monitoring Devices 1.4816 0.459 3.231 0.001 0.583 2.380
Price Guides & Publications -5.421e-15 5.43e-15 -0.998 0.318 -1.61e-14 5.22e-15
Print 0.1668 0.333 0.501 0.616 -0.486 0.819
Printers, Scanners & Supplies 0.5593 0.292 1.918 0.055 -0.012 1.131
Printmaking 0.3174 0.426 0.745 0.456 -0.517 1.152
Puffer 0.5405 0.220 2.458 0.014 0.110 0.971
Pumps 0.1649 0.217 0.761 0.447 -0.260 0.590
Purse 1.6621 0.515 3.226 0.001 0.652 2.672
Puzzle -0.4801 0.650 -0.738 0.460 -1.754 0.794
Puzzles -0.0910 0.227 -0.401 0.688 -0.536 0.354
Quilt 1.5653 1.037 1.510 0.131 -0.467 3.598
Quilts 1.1870 0.659 1.800 0.072 -0.105 2.479
Racks, Shelves & Drawers 0.3602 0.390 0.924 0.356 -0.404 1.124
Radio -3.975e-15 3.53e-15 -1.127 0.260 -1.09e-14 2.94e-15
Raincoat 0.1892 0.228 0.829 0.407 -0.258 0.637
Rainwear 0.2433 0.643 0.378 0.705 -1.017 1.503
Reference -0.2511 0.322 -0.779 0.436 -0.883 0.381
Refrigerators -2.464e-15 2.8e-15 -0.880 0.379 -7.95e-15 3.02e-15
Relaxed 0.5998 0.237 2.527 0.012 0.135 1.065
Religion 1.0753 0.692 1.555 0.120 -0.280 2.431
Religion & Spirituality 0.3074 0.261 1.178 0.239 -0.204 0.819
Religious 0.3522 0.417 0.845 0.398 -0.465 1.170
Replacement Parts & Tools -0.4556 0.641 -0.711 0.477 -1.711 0.800
Ring 0.7754 0.263 2.948 0.003 0.260 1.291
Rings 0.3215 0.238 1.351 0.177 -0.145 0.788
Salt and Pepper Shakers -0.0287 0.618 -0.046 0.963 -1.240 1.183
Sandals 0.0990 0.216 0.459 0.646 -0.324 0.522
Satchel 1.0372 0.211 4.919 0.000 0.624 1.450
Scale Dollhouse Miniature -0.2777 0.547 -0.508 0.612 -1.350 0.795
Scale Models 0.1923 0.547 0.352 0.725 -0.880 1.265
Scarf 0.6598 0.407 1.620 0.105 -0.138 1.458
Scarves & Wraps 0.2809 0.203 1.382 0.167 -0.118 0.679
School Supplies 0.2668 0.224 1.191 0.234 -0.172 0.706
Sci-Fi, Fantasy -0.0605 0.352 -0.172 0.863 -0.750 0.629
Science 3.57e-15 5.72e-15 0.624 0.533 -7.65e-15 1.48e-14
Science & Math 0.8521 0.338 2.519 0.012 0.189 1.515
Scifi -0.1411 0.692 -0.204 0.838 -1.497 1.215
Scoop Neck 0.2507 0.232 1.079 0.281 -0.205 0.706
Scrapbooking 0.2212 0.502 0.441 0.659 -0.762 1.204
Screen Protectors -0.9674 0.296 -3.271 0.001 -1.547 -0.388
Scrubs & Body Treatments 0.5725 0.243 2.358 0.018 0.097 1.048
Sculptures -0.0039 0.342 -0.012 0.991 -0.674 0.666
Sets 0.5716 0.224 2.557 0.011 0.133 1.010
Sets & Kits 0.1748 0.353 0.495 0.621 -0.517 0.867
Sewing 0.2074 0.859 0.241 0.809 -1.477 1.892
Shampoo 1.3318 0.632 2.108 0.035 0.093 2.570
Shampoo & Conditioner Sets 0.4982 0.321 1.551 0.121 -0.131 1.128
Shampoo Plus Conditioner 0.8180 0.453 1.804 0.071 -0.071 1.707
Shampoos 0.1539 0.335 0.459 0.646 -0.503 0.810
Shams, Bed Skirts & Bed Frame Draperies -0.1470 0.770 -0.191 0.849 -1.657 1.363
Shawl 2.691e-16 5.33e-16 0.505 0.614 -7.75e-16 1.31e-15
Sheets & Pillowcases 0.7768 0.644 1.206 0.228 -0.485 2.039
Shipping Supplies -0.0734 0.221 -0.332 0.740 -0.507 0.360
Shirt 0.3212 0.299 1.073 0.283 -0.265 0.908
Shirts & Tops 0.1399 0.212 0.659 0.510 -0.276 0.556
Shoes 0.5400 0.197 2.741 0.006 0.154 0.926
Shopping Cart Covers 0.1109 0.400 0.277 0.782 -0.673 0.895
Shorts 0.1353 0.212 0.638 0.523 -0.280 0.551
Shoulder Bag 0.7052 0.206 3.429 0.001 0.302 1.108
Shrug 0.1629 0.344 0.473 0.636 -0.512 0.838
Signs 2.3779 0.690 3.448 0.001 1.026 3.729
Skateboard 0.4830 0.261 1.854 0.064 -0.028 0.994
Skin Care 0.5184 0.517 1.002 0.316 -0.496 1.533
Skirt 0.5228 0.351 1.490 0.136 -0.165 1.210
Skirt Suit 0.8298 0.482 1.721 0.085 -0.115 1.775
Skirts 0.2779 0.409 0.679 0.497 -0.524 1.080
Skirts, Skorts & Dresses 0.1405 0.224 0.626 0.531 -0.299 0.580
Slim, Skinny 0.4940 0.208 2.377 0.017 0.087 0.901
Slippers -0.0847 0.221 -0.384 0.701 -0.517 0.348
Small Animal -0.4519 0.713 -0.634 0.526 -1.849 0.945
Small Appliances 0.8813 0.284 3.100 0.002 0.324 1.439
Snowboard 0.9656 0.296 3.257 0.001 0.385 1.547
Snowsuits & Bibs 0.8651 0.375 2.305 0.021 0.129 1.601
Soaps & Cleansers 0.7410 0.571 1.298 0.194 -0.378 1.860
Soccer 0.0548 0.157 0.350 0.727 -0.252 0.362
Socks -0.2222 0.214 -1.036 0.300 -0.642 0.198
Souvenir 0.2815 0.215 1.308 0.191 -0.140 0.703
Space Heaters -0.2196 0.333 -0.659 0.510 -0.872 0.433
Sports -0.3552 0.299 -1.187 0.235 -0.942 0.231
Sports & Outdoor Play 0.1840 0.241 0.764 0.445 -0.288 0.656
Sports Bras 0.0575 0.213 0.270 0.787 -0.360 0.475
Sports Nutrition 0.5829 0.275 2.120 0.034 0.044 1.122
St Patrick's 0.6589 0.708 0.931 0.352 -0.728 2.046
Stained Glass -3.384e-16 1.69e-16 -2.008 0.045 -6.69e-16 -8.05e-18
Stamps 0.5058 0.519 0.974 0.330 -0.512 1.523
Standard -0.7803 0.671 -1.162 0.245 -2.096 0.536
Stationery -0.3208 0.509 -0.630 0.529 -1.319 0.678
Stationery & Party Supplies -0.4998 0.311 -1.609 0.108 -1.109 0.109
Sticker -0.9097 0.498 -1.828 0.068 -1.885 0.066
Storage & Containers 0.0568 0.260 0.219 0.827 -0.452 0.566
Storage & Organization 0.2103 0.271 0.776 0.438 -0.321 0.741
Straight Leg 0.4546 0.216 2.101 0.036 0.031 0.879
Straight, Pencil 0.5554 0.235 2.367 0.018 0.095 1.015
Strategy Guides -0.2596 0.365 -0.712 0.476 -0.974 0.455
Strength training -0.5994 0.516 -1.162 0.245 -1.610 0.412
Stringed Instruments 0.0095 0.257 0.037 0.970 -0.494 0.514
Studio Recording Equipment 1.7611 0.337 5.225 0.000 1.100 2.422
Stuffed Animals & Plush 0.0826 0.205 0.403 0.687 -0.319 0.484
Styling Products 0.1225 0.319 0.384 0.701 -0.502 0.747
Styling Tools 0.5116 0.340 1.507 0.132 -0.154 1.177
Sun 0.4165 0.262 1.589 0.112 -0.097 0.930
Sunglasses 0.4750 0.200 2.372 0.018 0.083 0.867
Supplies 0.4039 0.351 1.152 0.249 -0.283 1.091
Sweater 0.2189 0.229 0.955 0.339 -0.230 0.668
Sweatercoat 1.1853 0.247 4.793 0.000 0.701 1.670
Sweaters -0.0996 0.407 -0.245 0.807 -0.898 0.699
Sweatshirt, Pullover 0.7581 0.359 2.109 0.035 0.054 1.463
Sweeping 2.9052 0.672 4.325 0.000 1.589 4.222
Swim Briefs 0.2518 0.534 0.471 0.637 -0.795 1.298
Swim Trunks -0.0115 0.148 -0.078 0.938 -0.301 0.278
Swimwear 0.1195 0.206 0.581 0.562 -0.284 0.523
Swings, Jumpers & Bouncers 0.8199 0.384 2.136 0.033 0.068 1.572
T-Shirts 0.1685 0.201 0.839 0.402 -0.225 0.562
T-shirts 0.2299 0.256 0.899 0.369 -0.271 0.731
Tag 0.1600 0.713 0.225 0.822 -1.237 1.557
Tank 0.0870 0.276 0.316 0.752 -0.453 0.627
Tank, Cami -0.0273 0.201 -0.136 0.892 -0.422 0.367
Tape -0.4327 0.310 -1.398 0.162 -1.039 0.174
Tapestries 0.6214 0.259 2.402 0.016 0.114 1.129
Teacup -0.1583 0.836 -0.189 0.850 -1.797 1.481
Teapot 0.3108 0.717 0.433 0.665 -1.095 1.717
Teethers 0.2975 0.513 0.580 0.562 -0.708 1.303
Teething Relief 0.5458 0.513 1.064 0.287 -0.460 1.551
Televisions -0.0187 0.379 -0.049 0.961 -0.761 0.723
Tennis & Racquets -0.0901 0.313 -0.288 0.774 -0.704 0.524
Thanksgiving 0.4374 0.661 0.662 0.508 -0.858 1.733
Thermal Underwear 1.1140 0.266 4.194 0.000 0.593 1.635
Thermometers -0.4435 0.413 -1.073 0.283 -1.253 0.366
Three Button 0.0820 0.993 0.083 0.934 -1.864 2.028
Ties -0.0699 0.234 -0.298 0.765 -0.529 0.389
Tires & Wheels 0.1547 0.399 0.388 0.698 -0.628 0.937
Toiletry Kits 1.2956 0.655 1.977 0.048 0.011 2.580
Tools & Equipment 0.6560 0.131 4.997 0.000 0.399 0.913
Toothbrushes 0.1424 0.362 0.393 0.694 -0.568 0.852
Top & T-shirts -0.0140 0.201 -0.070 0.944 -0.408 0.380
Tops & Blouses 0.0599 0.218 0.275 0.783 -0.367 0.487
Tops & T-Shirts 0.3069 0.197 1.554 0.120 -0.080 0.694
Tote 1.2534 0.526 2.383 0.017 0.223 2.284
Totes & Shoppers 0.5712 0.206 2.771 0.006 0.167 0.975
Towels 0.6367 0.490 1.300 0.194 -0.324 1.597
Toy -0.0987 0.316 -0.313 0.755 -0.718 0.520
Toy Remote Control & Play Vehicles 0.6101 0.215 2.839 0.005 0.189 1.031
Toys -0.4746 0.690 -0.688 0.491 -1.826 0.877
Track & Sweat Pants 0.6702 0.361 1.856 0.063 -0.037 1.378
Track & Sweat Suits 1.2779 0.436 2.933 0.003 0.424 2.132
Track Jacket 1.1046 0.705 1.566 0.117 -0.278 2.487
Tracksuits & Sweats 0.5897 0.214 2.761 0.006 0.171 1.008
Training Pants 0.3861 0.357 1.082 0.279 -0.313 1.085
Trash Bags 1.1843 0.516 2.296 0.022 0.173 2.195
Travel Beds 1.3507 0.344 3.927 0.000 0.677 2.025
Travel Systems 1.2610 0.406 3.105 0.002 0.465 2.057
Tray 0.8955 0.458 1.957 0.050 -0.001 1.792
Trench 0.2918 0.231 1.265 0.206 -0.160 0.744
Tricycles, Scooters & Wagons 1.3662 0.480 2.849 0.004 0.426 2.306
Trim 0.1691 0.656 0.258 0.797 -1.116 1.454
Tripods & Supports 0.6536 0.670 0.975 0.330 -0.660 1.968
Tshirt 0.1548 0.325 0.477 0.633 -0.481 0.791
Tumbler 0.8253 0.579 1.425 0.154 -0.310 1.961
Tunic 0.5222 0.202 2.582 0.010 0.126 0.919
Turtleneck -0.1741 0.262 -0.665 0.506 -0.687 0.339
Turtleneck, Mock 0.2785 0.242 1.152 0.249 -0.195 0.752
Tweezers -0.1952 0.365 -0.535 0.593 -0.911 0.520
Two Button 0.5363 0.783 0.685 0.494 -0.999 2.072
Two-Piece 0.0389 0.089 0.437 0.662 -0.135 0.213
V-Neck 0.1271 0.221 0.574 0.566 -0.307 0.561
VHS 0.0553 0.310 0.178 0.858 -0.552 0.663
Vacuums & Floor Care 1.4424 0.206 7.006 0.000 1.039 1.846
Valentine 0.2544 0.654 0.389 0.697 -1.028 1.536
Varsity/Baseball 0.4350 0.371 1.171 0.242 -0.293 1.163
Vase 0.2749 0.451 0.610 0.542 -0.608 1.158
Vases 0.3864 0.287 1.346 0.178 -0.176 0.949
Vest 0.1259 0.209 0.601 0.548 -0.284 0.536
Vest, Sleeveless 0.6063 0.239 2.534 0.011 0.137 1.075
Vests 0.1910 0.313 0.610 0.542 -0.422 0.805
Video Game 0.6229 0.641 0.972 0.331 -0.633 1.879
Video Gaming Merchandise 0.0516 0.212 0.244 0.807 -0.363 0.466
Vintage -0.1464 0.338 -0.433 0.665 -0.809 0.516
Vinyl 0.3621 0.166 2.177 0.029 0.036 0.688
Volleyball -0.4785 0.249 -1.919 0.055 -0.967 0.010
Walkers 0.4659 0.546 0.853 0.393 -0.604 1.536
Wall Decor -0.0864 0.298 -0.290 0.772 -0.671 0.498
Wall Hanging 0.3495 0.336 1.039 0.299 -0.310 1.008
Wallet 0.7461 0.451 1.655 0.098 -0.137 1.630
Wallets 0.5415 0.199 2.719 0.007 0.151 0.932
Washcloths & Towels 0.5640 0.528 1.068 0.286 -0.471 1.599
Watch 1.1961 0.287 4.164 0.000 0.633 1.759
Watches 1.0413 0.200 5.205 0.000 0.649 1.433
Water Coolers & Filters 0.1622 0.435 0.373 0.709 -0.690 1.015
Water Sports 0.5053 0.279 1.813 0.070 -0.041 1.052
Waxing 0.2819 0.282 0.998 0.318 -0.272 0.835
Wide Leg 0.6410 0.411 1.560 0.119 -0.165 1.447
Wind & Woodwind Instruments 0.3404 0.565 0.603 0.547 -0.767 1.448
Windbreaker 0.3733 0.209 1.786 0.074 -0.036 0.783
Window Treatments 0.6251 0.261 2.395 0.017 0.114 1.137
Wine Accessories 0.1017 0.281 0.363 0.717 -0.448 0.652
Wine, Beer & Beverage Coolers -0.0248 0.132 -0.188 0.851 -0.283 0.234
Wipes & Holders 0.0483 0.184 0.262 0.794 -0.313 0.410
Women 0.4987 0.212 2.347 0.019 0.082 0.915
Wool 0.6171 0.271 2.280 0.023 0.087 1.148
Work & Safety 0.5693 0.305 1.870 0.062 -0.028 1.166
Wrap 0.4760 0.220 2.159 0.031 0.044 0.908
Wristlet 1.2344 0.519 2.379 0.017 0.218 2.251
Writing 0.1662 0.232 0.716 0.474 -0.289 0.622
Yarn 0.1307 0.349 0.375 0.708 -0.553 0.814
Yoga & Pilates -1.1046 0.485 -2.279 0.023 -2.055 -0.155
iPad/Tablet/eBook Access 0.7929 0.271 2.931 0.003 0.263 1.323
iPad/Tablet/eBook Readers 1.1929 0.269 4.434 0.000 0.666 1.720
==============================================================================
Omnibus: 8174.231 Durbin-Watson: 1.998
Prob(Omnibus): 0.000 Jarque-Bera (JB): 17237.245
Skew: 0.523 Prob(JB): 0.00
Kurtosis: 4.701 Cond. No. 3.65e+18
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is 3.89e-32. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.
Important Note:¶
- Not always R-squared is the best parameter to judge our model as sometimes no matter what we do, we can never get a proper R-squared value because the way in which the data is built. Hence we look after other deciding parameter called RMSLE.
- We are calling RMSLE (Root Mean Squared log Error) instead of
RMSEbecause we have taken the log of our dependent variable as it wasn't nomrally distributed.
Insights from OLS model:
- As we can see that the R-squared is almost close to 1 which means the predicted value of the training data is very similar to that of the test data
- The 2nd warning tells us that there are strong multi-collinearity problems or that the design matrix is singular because of the one hot encoding and many unique values in our data columns
- Hence this is one of the major drawbacks of this model in our case
- Other factors like Skewness (0.5), Kurtosis (4.7) i.e. almost close to Mesokurtic curve and Auto-correlation (1.99) tell us that model is optimal, however we still can't use this model to know the RMSLE as well as to predict the values
- For predicting the values for testing data we use linear regression and see if we can overcome the OLS model's drawbacks
2. Linear Regression Model:¶
In [86]:
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(x_train, y_train)
y_pred_train = model.predict(x_train) # Predicting the value of train to compare with actual value
y_pred_test = model.predict(x_test) # Predicting the value of test data to compare with actual value
from sklearn.metrics import mean_squared_error
print("RMSLE value of Training Data is {a}".format(a=np.sqrt(mean_squared_error(y_train,y_pred_train))))
print("RMSLE value of Testing Data is {a}".format(a=np.sqrt(mean_squared_error(y_test,y_pred_test))))
RMSLE value of Training Data is 0.5998243479848635 RMSLE value of Testing Data is 2023340540.2473302
Insights from Linear Model:
- We can observe that training data has very less rmsle but the testing data the rmsle is extremely high.
Problem:
- The data is containing multi-collinearity which can be seen in the RMSLE of testing data
Solution:
- Almost 99% of our columns are the dummies representing the categorical data and we cannot remove them and it would be futile to check the VIF of over 3500 columns
- Hence, we have to use algorithms which we can fine tune to optimize our model
- This leads us to a conclusion that there is a need to build models which can help us apply techniques like Dimension reduction, Regularization, Boosting or Bagging. Let's check them out one by one
- In ML, the ideal model is the one which has low bias i.e it can capture true relationship and low variability by producing consistent predictions across different datasets
The methods used to do this are -
• Regularization • Boosting • Bagging
3. Ridge Regression:¶
Overview:
- Ridge Regression comes under the Reglarization method
- It provides a way to create a simple model with great explanatory power when number of predictor variables in a dataset are very high and not very significant or when a datset has multi-collinearity
- It uses a type of Shrinkage Parameter called ridge estimator which belongs to a class of L2 regularization (λ)
- L2 regularization adds a penalty which equals the square of the magnitude of co-efficient
- All co-effs are shrunk by the same factor
- When λ = 0, ridge regression equals least squares regression and when λ = infinity, all co-effs are shrunk to 0
- Our goal here is to find the perfect λ value because as λ increases, error due to bias increases and as λ decreases, error due to variance increases
Note:- In python, While modelling the λ is considered as alpha
In [87]:
from sklearn.linear_model import Ridge
# from sklearn.model_selection import GridSearchCV
# parameters = {"alpha":[0.01,0.1,0,1,10,100]}
# ridgeReg = Ridge(solver = "lsqr", fit_intercept=False)
# lr_reg = GridSearchCV(ridgeReg,param_grid =parameters,n_jobs=-1)
# lr_reg.fit(x_train, y_train)
# By applying GridsearchCV, we get to know that the best parameter for the model is given by
# lr_reg.best_params_
# Output- {'alpha': 0.1}
ridgeReg = Ridge(alpha=0.1,solver="lsqr",fit_intercept=False)
ridgeReg.fit(x_train,y_train)
y_pred = ridgeReg.predict(x_test)
y_pred_train = ridgeReg.predict(x_train)
from sklearn.metrics import r2_score,mean_squared_error
rmse_test_ridge = np.sqrt(mean_squared_error(y_test,y_pred))
rmse_train_ridge = np.sqrt(mean_squared_error(y_train,y_pred_train))
print("RMSLE value of Training Data is {a}".format(a=rmse_train_ridge))
print("RMSLE value of Testing Data is {a}".format(a=rmse_test_ridge))
RMSLE value of Training Data is 0.6130745863069402 RMSLE value of Testing Data is 0.6247986429568546
Interpretation of RMSLE value:¶
- It is a way of figuring out how much a model disagrees with the actual data
- The lower this number is, the better the fit of the model
- It is preferred that the value of RMSLE should be close to the minimum value of the response variable in test data
Checking the min and max values of y_test corresponding to our RMSLE value 0.62
In [88]:
print('Min value of y_test is', min(y_test))
print('Max value of y_test is', max(y_test))
Min value of y_test is 1.0986122886681098 Max value of y_test is 7.498869733976931
Insight from Ridge Model:
As we can see that 0.62 is close to 1.09, we can conclude that our model is able to predict the value close to the actual value
4. SGD(Stochastic Gradient Descent) Model:¶
Overview:
- In statistics, machine learning and data science fields, we optimize a lot of stuff
- When we fit a line with linear regression, we optimize the slope and intercept
- In logistic regression, we optimize the squiggly line
- In a similar way Gradient descent also helps optimize such things and much more
- In this case we have almost half billion data points and hence computations can take a long time
- This method uses a randomly selected subset of data at every step rather than full dataset
- This reduces the time spent calculating derivatives of loss function (Sum of squared Residuals)
In [89]:
from sklearn.linear_model import SGDRegressor,Ridge
# parameters = {"alpha": [0.01,0.1,0,1,10,100],
# "l1_ratio": [0.4,0.5,0.6,0.7,0.8],
# }
#
# model_sgd = SGDRegressor(loss="squared_loss",penalty="l2",
# learning_rate="invscaling",max_iter=100,
# fit_intercept=False)
#
# model_SGD = GridSearchCV(model_sgd,param_grid=parameters)
# model_SGD.fit(x_train,y_train)
# By applying GridsearchCV, we get to know that the best parameter for the model is given by
# print(model_SGD.best_params_)
# Output - {'alpha': 0, 'l1_ratio': 0.4}
model_SGD = SGDRegressor(loss="squared_loss",penalty="l2",
learning_rate="invscaling",max_iter=100,
fit_intercept=False,alpha=0,l1_ratio=0.4)
model_SGD.fit(x_train,y_train)
y_pred_train_sgd = model_SGD.predict(x_train)
y_pred_test_sgd = model_SGD.predict(x_test)
from sklearn.metrics import r2_score,mean_squared_error
rmse_train = np.sqrt(mean_squared_error(y_train,y_pred_train_sgd))
rmse_test = np.sqrt(mean_squared_error(y_test,y_pred_test_sgd))
print("RMSLE value of Training Data is {a}".format(a=rmse_train))
print("RMSLE value of Testing Data is {a}".format(a=rmse_test))
RMSLE value of Training Data is 0.6344507517916266 RMSLE value of Testing Data is 0.6421330592052263
Insight from SGD model:
- We can see that the RMSLE is 0.64 which is slightly greater than the RMSLE in the Ridge Model of 0.62
5. Random Forest Regression Model:¶
- Decision trees have the possibility of overfitting and have inaccuracy for predictive learning as not all the variables give less impurity at each split
- Also they are not flexible with new data
- This problem is easily overcome by random forests as it combines the simplicity of the decision trees with flexibility resulting in a vast improvement in accuracy
- Bagging method is used to solve the problem we are facing
- Variance decreases because of the sub samples created and 1/3rd of the observations are left for validation purpose
Checking for best Parameters using Grid search -
In [90]:
# from sklearn.model_selection import RandomizedSearchCV
# param_dist = {'n_estimators': [10,50,100,150,160,200,300],
# 'min_samples_split': [2,3,5,6],
# "max_depth":[None,10,20,40,60,80]
# }
# regr1 = RandomForestRegressor()
# n_iter_search = 50
# regr1 = RandomizedSearchCV(regr1, param_distributions=param_dist,
# n_iter=n_iter_search, cv=3,n_jobs=-1)
# regr1.fit(x_train, y_train)
# print(regr1.best_params_)
# output - {'n_estimators': 200, 'min_samples_split': 3,'max_depth=40'}
In [19]:
from sklearn.ensemble import RandomForestRegressor
model_rf = RandomForestRegressor(n_jobs=-1,min_samples_split=3,n_estimators=200,max_depth=40)
model_rf.fit(x_train,y_train)
y_pred_train_rf = model_rf.predict(x_train)
y_pred_test_rf = model_rf.predict(x_test)
from sklearn.metrics import mean_squared_error
rmse_train_rf = np.sqrt(mean_squared_error(y_train,y_pred_train_rf))
rmse_test_rf = np.sqrt(mean_squared_error(y_test,y_pred_test_rf))
print("RMSLE value of Training Data is {a}".format(a=rmse_train_rf))
print("RMSLE value of Testing Data is {a}".format(a=rmse_test_rf))
RMSLE value of Training Data is 0.5752236849538379 RMSLE value of Testing Data is 0.6190771439660767
Insight from Random Forest Model:
- RMSLE value we obtained here 0.61 is a tiny bit, yet better than the RMSLE we got in Ridge model, thus the best model so far
Step 3: Final Conclusion¶
In [1]:
from prettytable import PrettyTable
x = PrettyTable()
x.field_names = ["Models", "HyperParameters used", "RMSLE"]
x.add_row(["Ordinary Least Squares Regression", "---", '---'])
x.add_row(["Linear Regression", "---", 2023340540.2473302])
x.add_row(["Ridge Regression", "alpha", 0.62])
x.add_row(["SGD Regressor","alpha, l1_ratio" , 0.64])
x.add_row(["Random Forest Regressor ","n_estimators, min_samples_split, max_depth" , 0.61])
print(x)
+-----------------------------------+--------------------------------------------+--------------------+ | Models | HyperParameters used | RMSLE | +-----------------------------------+--------------------------------------------+--------------------+ | Ordinary Least Squares Regression | --- | --- | | Linear Regression | --- | 2023340540.2473302 | | Ridge Regression | alpha | 0.62 | | SGD Regressor | alpha, l1_ratio | 0.64 | | Random Forest Regressor | n_estimators, min_samples_split, max_depth | 0.61 | +-----------------------------------+--------------------------------------------+--------------------+
- We couldn't find
RMSLEin OLS model and the Linear model had a very huge value - Multi-collinearity in the data lead us to build models where we could
fine tuneit according to the best parameters provided by grid search mechanism - Out of all the models we applied, Random Forest Regressor gave us the
bestand thelowestRMSLE value 0.61 - Hence we can deploy this model at the clients machine so that sellers can get an estimate of the price they should set for their products
- Another solution would be making an app wherein the seller will enter the parameters one by one and after submitting he will get the result as the Suggested Price for the inputs provided by him/her